# Why noise in GAN's are random?

I was learning about GAN's and how it can be used for creating images of cat, dog, mouse and bird. This is how I think in supervised learning system we train the features and labels to generate images.

cat,dog,mouse, bird = 0,1,2,3

X  Y
1  dog.jpg
3  bird.jpg
0  cat.jpg
0  cat1.jpg
.  .
.  .

train(X,Y)


after training for generating a image

predict(0) #generates cat image
predict(2) #generates mouse image


Because GAN is an unsupervised learning system it learns to generate images using adversarial training. What I have seen in others implementation of GANs is that they add a noise as an input to the generator. The noise is generally a continuous number between 0 and 1. Why do they add random number as noise? why cant they simply add some numbers for example cat,dog,mouse, bird = [0,1,2,3]?