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: "do gan loss functions really matter"
  Abstract: In this paper, we address the recent controversy between Lipschitz regularization and the choice of loss function for the training of Generative Adversarial Networks (GANs). One side argues that the success of the GAN training should be attributed to the choice of loss function [16, 2, 5], while the other suggests that the Lipschitz regularization is the key to good results [17, 3, 18, 19]. We provide a theoretical and experimental analysis of how Lipschitz regularization interacts with the loss function to derive the following insights: (i) We show that popular GANs (NS-GAN [4], LS-GAN [16], WGAN [2]) perform equally well when the discriminator is regularized with a small Lipschitz constant, but the performance in terms of quality and diversity gets worse for larger Lipschitz constants, except for WGAN. (ii) We show that all loss functions degenerate to linear ones for small Lipschitz constants to explain why the performance of these GANs is similar. For higher Lipschitz constants, we observe that only WGAN performs well while NS-GAN and LS-GAN break down. For lower Lipschitz constants, NS-GAN and LS-GAN perform similarly to WGAN only because they degenerate to the WGAN loss. In order to further illustrate this issue, we demonstrate that even "ridiculous" loss functions such as sin and exp have similar performance to NS-GAN, LS-GAN, and WGAN, when small Lipschitz constants are used.

Connected through hyperedge HE-1 (induced by source paper "encoding invariances in deep generative models"):
  with members:
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "spatially constrained generative adversarial networks for conditional image generation"
    - "constrained image generation using binarized neural networks with decision procedures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "bias and generalization in deep generative models an empirical study"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "on the estimation of the wasserstein distance in generative models"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

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)