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: "an online learning approach to generative adversarial networks"
  Abstract: We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training.

Connected through hyperedge HE-1 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization"):
  with members:
    - "from darwin to poincar e and von neumann recurrence and cycles in evolutionary and algorithmic game theory"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "a theory of regularized markov decision processes"
    - "openspiel a framework for reinforcement learning in games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the mechanics of n player differentiable games"
    - "stable opponent shaping in differentiable games"
    - "modeling friends and foes"

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"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-3 (induced by source paper "correlated adversarial imitation learning"):
  with members:
    - "causal confusion in imitation learning"
    - "maximum causal tsallis entropy imitation learning"
    - "quantifying generalization in reinforcement learning"
    - "learning in non convex games with an optimization oracle"
    - "on convergence and stability of gans"
    - "efficient regret minimization in non convex games"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "a mean field analysis of two player zero sum games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "deep clustering for unsupervised learning of visual features"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "stabilizing generative adversarial network training a survey"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "multi objective training of generative adversarial networks with multiple discriminators"
    - "evaluating gans via duality"
    - "convergence problems with generative adversarial networks gans"
    - "the relativistic discriminator a key element missing from standard gan"
    - "evolutionary generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"

Connected through hyperedge HE-6 (induced by source paper "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"):
  with members:
    - "on gradient based learning in continuous games"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "a theoretical analysis of deep q learning"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "local saddle point optimization a curvature exploitation approach"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"

Connected through hyperedge HE-7 (induced by source paper "what is local optimality in nonconvex nonconcave minimax optimization"):
  with members:
    - "on gradient based learning in continuous games"
    - "introduction to online convex optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "local saddle point optimization a curvature exploitation approach"
    - "on the convergence of gradient based learning in continuous games"
    - "learning in non convex games with an optimization oracle"
    - "negative momentum for improved game dynamics"
    - "finding mixed nash equilibria of generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "on gradient descent ascent for nonconvex concave minimax problems"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "local saddle point optimization a curvature exploitation approach"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "solving approximate wasserstein gans to stationarity"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of 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.LG(Machine Learning)