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: "banach wasserstein gan"
  Abstract: Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on a notion of distance between individual images, which induces a notion of distance between probability distributions of images. So far the community has considered $\ell^2$ as the underlying distance. We generalize the theory of WGAN with gradient penalty to Banach spaces, allowing practitioners to select the features to emphasize in the generator. We further discuss the effect of some particular choices of underlying norms, focusing on Sobolev norms. Finally, we demonstrate the impact of the choice of norm on model performance and show state-of-the-art inception scores for non-progressive growing GANs on CIFAR-10.

Connected through hyperedge HE-1 (induced by source paper "s flow gan"):
  with members:
    - "quantifying the reality gap in robotic manipulation tasks"
    - "the perception distortion tradeoff"
    - "towards accurate generative models of video a new metric challenges"
    - "large scale gan training for high fidelity natural image synthesis"
    - "large receptive field networks for high scale image super resolution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "sliced wasserstein generative models"):
  with members:
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "implicit competitive regularization in gans"):
  with members:
    - "coherent gradients an approach to understanding generalization in gradient descent based optimization"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a gradual semi discrete approach to generative network training via explicit wasserstein minimization"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "implicit regularization in deep matrix factorization"
    - "implicit regularization in over parameterized neural networks"

Connected through hyperedge HE-4 (induced by source paper "best of many samples distribution matching"):
  with members:
    - "joint wasserstein autoencoders for aligning multimodal embeddings"
    - "learning implicit generative models with the method of learned moments"
    - "mixture density generative adversarial networks"
    - "cramer wold autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "can vaes generate novel examples"

Connected through hyperedge HE-5 (induced by source paper "orthogonal wasserstein gans"):
  with members:
    - "on gans and gmms"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"

Connected through hyperedge HE-6 (induced by source paper "adversarial lipschitz regularization"):
  with members:
    - "lipschitz generative adversarial nets"
    - "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"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-7 (induced by source paper "quality aware generative adversarial networks"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "stabilizing training of generative adversarial networks through regularization"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "towards efficient and unbiased implementation of lipschitz continuity in gans"):
  with members:
    - "lipschitz generative adversarial nets"
    - "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"
    - "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.CV(Computer Vision and Pattern Recognition)