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: "on the convergence and robustness of training gans with regularized optimal transport"
  Abstract: Generative Adversarial Networks (GANs) are one of the most practical methods for learning data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions. Unfortunately, minimizing the Wasserstein distance between the data distribution and the generative model distribution is a computationally challenging problem as its objective is non-convex, non-smooth, and even hard to compute. In this work, we show that obtaining gradient information of the smoothed Wasserstein GAN formulation, which is based on regularized Optimal Transport (OT), is computationally effortless and hence one can apply first order optimization methods to minimize this objective. Consequently, we establish theoretical convergence guarantee to stationarity for a proposed class of GAN optimization algorithms. Unlike the original non-smooth formulation, our algorithm only requires solving the discriminator to approximate optimality. We apply our method to learning MNIST digits as well as CIFAR-10images. Our experiments show that our method is computationally efficient and generates images comparable to the state of the art algorithms given the same architecture and computational power.

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"
    - "lipschitz generative adversarial nets"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "smoothness and stability in gans"):
  with members:
    - "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"
    - "beyond local nash equilibria for adversarial networks"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "connecting gans and mfgs"):
  with members:
    - "smoothness and stability in gans"
    - "improving gans using optimal transport"
    - "f gans in an information geometric nutshell"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "semantic segmentation using adversarial networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-4 (induced by source paper "r enyi fair inference"):
  with members:
    - "fair logistic regression an adversarial perspective"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "scalable fair clustering"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "tunable measures for information leakage and applications to privacy utility tradeoffs"
    - "fair clustering through fairlets"

Connected through hyperedge HE-5 (induced by source paper "deconstructing generative adversarial networks"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "stabilizing gan training with multiple random projections"
    - "approximability of discriminators implies diversity in gans"
    - "a convex duality framework for gans"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on the implicit assumptions of gans"
    - "solving approximate wasserstein gans to stationarity"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "min max optimization without gradients convergence and applications to adversarial ml"):
  with members:
    - "beyond adversarial training min max optimization in adversarial attack and defense"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "robust optimization over multiple domains"
    - "spectral signatures in backdoor attacks"
    - "caad 2018 iterative ensemble adversarial attack"
    - "approximating nash equilibria for black box games a bayesian optimization approach"
    - "towards distributed coevolutionary gans"
    - "certified defenses for data poisoning attacks"

Connected through hyperedge HE-7 (induced by source paper "on the estimation of the wasserstein distance in generative models"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "a style based generator architecture for generative adversarial networks"
    - "do gan loss functions really matter"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "adversarial computation of optimal transport maps"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "on convergence and stability of gans"
    - "solving approximate wasserstein gans to stationarity"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "pacgan the power of two samples in generative adversarial networks"
    - "a geometric view of optimal transportation and generative model"

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)