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: "cycles in adversarial regularized learning"
  Abstract: Regularized learning is a fundamental technique in online optimization, machine learning and many other fields of computer science. A natural question that arises in these settings is how regularized learning algorithms behave when faced against each other. We study a natural formulation of this problem by coupling regularized learning dynamics in zero-sum games. We show that the system's behavior is Poincar\'e recurrent, implying that almost every trajectory revisits any (arbitrarily small) neighborhood of its starting point infinitely often. This cycling behavior is robust to the agents' choice of regularization mechanism (each agent could be using a different regularizer), to positive-affine transformations of the agents' utilities, and it also persists in the case of networked competition, i.e., for zero-sum polymatrix games.

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 "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "bandit learning in concave n person games"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a generic approach for escaping saddle points"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-3 (induced by source paper "understanding and mitigating gradient pathologies in physics informed neural networks"):
  with members:
    - "machine learning in cardiovascular flows modeling predicting arterial blood pressure from non invasive 4d flow mri data using physics informed neural networks"
    - "variational physics informed neural networks for solving partial differential equations"
    - "the mechanics of n player differentiable games"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "covariant compositional networks for learning graphs"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-4 (induced by source paper "optimality and stability in non convex non concave min max optimization"):
  with members:
    - "training gans with centripetal acceleration"
    - "convergence behaviour of some gradient based methods on bilinear games"
    - "lower bounds and conditioning of differentiable games"
    - "linear lower bounds and conditioning of differentiable games"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-5 (induced by source paper "distributed no regret learning in multi agent systems"):
  with members:
    - "policy regret in repeated games"
    - "negative momentum for improved game dynamics"
    - "bandit learning in concave n person games"
    - "learning in time varying games"
    - "training gans with optimism"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "delay and cooperation in nonstochastic bandits"
    - "learning and efficiency in games with dynamic population"

Connected through hyperedge HE-6 (induced by source paper "local nash equilibria are isolated strict local nash equilibria in almost all zero sum continuous games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "robust adversarial reinforcement learning"
    - "training gans with optimism"

Connected through hyperedge HE-7 (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-8 (induced by source paper "differentiable game mechanics"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "re evaluating evaluation"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "strongly typed agents are guaranteed to interact safely"
    - "learning with opponent learning awareness"

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.GT(Computer Science and Game Theory)