If data is sparse, the posterior will still be sharply peaked around distributions in the gamma family, reducing the effective capacity of the model and minimizing overfitting.
given enough evidence in the data, the model will also deviate from the gamma-centered prior-depending on the kernel function chosen for the GP prior, any density function can in principle be represented.