Limit of Certainty Equivalent for Cumulative Prospect Theory

Question

I found a piece of code from a former colleague who seems to have copied it from another source (perhaps internet) that confused me a bit. Therein, the Certainty Equivalent (CE henceforth) for Cumulative Prospect Theory (here CPT) is calculated as

Function CEpos(cpt As Double, alpha As Double) As Double
    If alpha > 0 Then
        CEpos = cpt ^ (1 / alpha) 'OK
    ElseIf alpha = 0 Then
        CEpos = Exp(cpt) 
    Else
        CEpos = ((1 - cpt) ^ (1 / alpha)) - 1 
    End If
End Function

Function CEneg(cpt As Double, alpha As Double, lambda As Double) As Double
    If alpha > 0 Then
        CEneg = (-1) * (((-1) * cpt / lambda) ^ (1 / alpha)) 
    ElseIf alpha = 0 Then
        CEneg = (-1) * Exp((-1) * cpt / lambda)
    Else
        CEneg = 1 - (1 + cpt / lambda) ^ (1 / alpha)
    End If
End Function 

I am confused as the CE of CPT is supposed to converge to $\lim_{\alpha\rightarrow{0}} (CE)=e^{CPT}$. Perhaps this is too simple but i'm stuck as my guess is that for ${\alpha\rightarrow{0}}$ the agent is insensitive to variations in gains/losses, thus the CE is zero.

My idea was that for $CPT={CE}^{\alpha}$ the CPT approaches 1 as i could expand the CPT using a Taylor Series and evalute at $\alpha=0$ such that \begin{equation} \label{eq:1} CPT=1+\alpha*\log(CE)+\frac{1}{2}*{\alpha^{2}}*\log^{2}(CE)+O(CE^{3}) \end{equation} and thus CPT is 1 for all gains (similar for losses, there shifted by the loss aversion parameter).

Would anyone mind helping me out how to get to the expression above from the code? I don't see it yet (and yes: it seems simple but as i said i'm stuck..)

Thanks a lot in advance
Thomas