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: "lipschitz generative adversarial nets"
  Abstract: In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function. We show that GANs without restriction on the discriminative function space commonly suffer from the problem that the gradient produced by the discriminator is uninformative to guide the generator. By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem. We further show in the paper that the model with a compact dual form of Wasserstein distance, where the Lipschitz condition is relaxed, may also theoretically suffer from this issue. This implies the importance of Lipschitz condition and motivates us to study the general formulation of GANs with Lipschitz constraint, which leads to a new family of GANs that we call Lipschitz GANs (LGANs). We show that LGANs guarantee the existence and uniqueness of the optimal discriminative function as well as the existence of a unique Nash equilibrium. We prove that LGANs are generally capable of eliminating the gradient uninformativeness problem. According to our empirical analysis, LGANs are more stable and generate consistently higher quality samples compared with WGAN.

Connected through hyperedge HE-1 (induced by source paper "gans may have no nash equilibria"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"
    - "a convex duality framework for gans"

Connected through hyperedge HE-2 (induced by source paper "designing gans a likelihood ratio approach"):
  with members:
    - "generative adversarial networks an overview"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "on loss functions for deep neural networks in classification"
    - "energy based generative adversarial network"
    - "adam a method for stochastic optimization"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-3 (induced by source paper "variation network learning high level attributes for controlled input manipulation"):
  with members:
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "diagnosing and enhancing vae models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep learning based decoding for constrained sequence codes"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "sampling generative networks"
    - "glsr vae geodesic latent space regularization for variational autoencoder architectures"

Connected through hyperedge HE-4 (induced by source paper "adversarial lipschitz regularization"):
  with members:
    - "towards efficient and unbiased implementation of lipschitz continuity in gans"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "batch normalization is a cause of adversarial vulnerability"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "towards efficient and unbiased implementation of lipschitz continuity in gans"):
  with members:
    - "on the convergence of adam and beyond"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "on convergence and stability of gans"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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.LG(Machine Learning)