How does a zero centered activation functions like tanh helps in gradient decent?
$\tanh$ function is scaled standard sigmoid function $y=\frac{1}{1+e^{-ax}}$. Due to that scaling it has more steep gradient that standard sigmoid function. Steep gradient is important, because it makes backprop training faster and less likely to get stuck in zero-gradient area.  

Furthermore $y(0) \neq 0$ where $\tanh(0) = 0$. Sometimes it is very important, especially when dealing with normalized $[0,1]$ signals as inputs.