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: "a polynomial time nash equilibrium algorithm for repeated stochastic games"
  Abstract: We present a polynomial-time algorithm that always finds an (approximate) Nash equilibrium for repeated two-player stochastic games. The algorithm exploits the folk theorem to derive a strategy profile that forms an equilibrium by buttressing mutually beneficial behavior with threats, where possible. One component of our algorithm efficiently searches for an approximation of the egalitarian point, the fairest pareto-efficient solution. The paper concludes by applying the algorithm to a set of grid games to illustrate typical solutions the algorithm finds. These solutions compare very favorably to those found by competing algorithms, resulting in strategies with higher social welfare, as well as guaranteed computational efficiency.

Connected through hyperedge HE-1 (induced by source paper "theory of minds understanding behavior in groups through inverse planning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "machine theory of mind"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-2 (induced by source paper "foolproof cooperative learning"):
  with members:
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"
    - "robust learning equilibrium"

Connected through hyperedge HE-3 (induced by source paper "near optimal online egalitarian learning in general sum repeated matrix games"):
  with members:
    - "online reinforcement learning in stochastic games"
    - "optimism in reinforcement learning and kullback leibler divergence"

Connected through hyperedge HE-4 (induced by source paper "a multi agent reinforcement learning method for impression allocation in online display advertising"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "counterfactual multi agent policy gradients"
    - "real time bidding by reinforcement learning in display advertising"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "feedback control of real time display advertising"

Connected through hyperedge HE-5 (induced by source paper "mean field multi agent reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "learning with opponent learning awareness"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "opponent modeling in deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "cooperating with machines"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "navigating the topology of 2x2 games an introductory note on payoff families normalization and natural order"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "prosocial learning agents solve generalized stag hunts better than selfish ones"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "cooperating with machines"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "consequentialist conditional cooperation in social dilemmas with imperfect information"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "cooperating with machines"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "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)