human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "how does lipschitz regularization influence gan training"
  Abstract: Lipschitz regularization has shown great success in stabilizing GAN training. Despite the recent algorithmic progress (e.g., weight clipping [2], gradient penalty [5], spectral normalization [18]), the exact reason of its effectiveness is still not well understood. The common belief is that Lipschitz regularization works by keeping the weights of the discriminator network small and thereby bounding the neural network gradients. In this work, we discover an even more important function of Lipschitz regularization. It restricts the domain, range, and interval of attainable gradient values of loss functions, thereby preventing the backpropagation of vanishing or exploding gradients. By introducing the concept of domain scaling, we can decouple the effect of Lipschitz regularization on the discriminator network and the loss function. This enables us to show that discriminator networks with larger weights also perform well as long as the domain of the loss function is scaled down. Empirically, we verify our proposition on the MNIST, CIFAR10 and CelebA datasets.

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)