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 generalised method for empirical game theoretic analysis"
  Abstract: This paper provides theoretical bounds for empirical game theoretical analysis of complex multi-agent interactions. We provide insights in the empirical meta game showing that a Nash equilibrium of the meta-game is an approximate Nash equilibrium of the true underlying game. We investigate and show how many data samples are required to obtain a close enough approximation of the underlying game. Additionally, we extend the meta-game analysis methodology to asymmetric games. The state-of-the-art has only considered empirical games in which agents have access to the same strategy sets and the payoff structure is symmetric, implying that agents are interchangeable. Finally, we carry out an empirical illustration of the generalised method in several domains, illustrating the theory and evolutionary dynamics of several versions of the AlphaGo algorithm (symmetric), the dynamics of the Colonel Blotto game played by human players on Facebook (symmetric), and an example of a meta-game in Leduc Poker (asymmetric), generated by the PSRO multi-agent learning algorithm.

Connected through hyperedge HE-1 (induced by source paper "emergent coordination through competition"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deepmind control suite"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "re evaluating evaluation"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-2 (induced by source paper "openspiel a framework for reinforcement learning in games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "behaviour suite for reinforcement learning"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "open ended learning in symmetric zero sum games"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "re evaluating evaluation"

Connected through hyperedge HE-3 (induced by source paper "a generalized training approach for multiagent learning"):
  with members:
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "multiagent evaluation under incomplete information"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "openspiel a framework for reinforcement learning in games"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "episodic curiosity through reachability"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "malthusian reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "alpha rank multi agent evaluation by evolution"):
  with members:
    - "emergent coordination through competition"
    - "re evaluating evaluation"
    - "the exact computational complexity of evolutionarily stable strategies"
    - "learning dynamics and the co evolution of competing sexual species"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"

Connected through hyperedge HE-6 (induced by source paper "finding friend and foe in multi agent games"):
  with members:
    - "leveling the playing field fairness in ai versus human game benchmarks"
    - "theory of minds understanding behavior in groups through inverse planning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "learning to share and hide intentions using information regularization"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-7 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "multiagent evaluation under incomplete information"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "bandit learning in concave n person games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "safe and nested subgame solving for imperfect information games"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-8 (induced by source paper "multiagent evaluation under incomplete information"):
  with members:
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "re evaluating evaluation"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "giraffe using deep reinforcement learning to play chess"
    - "pagerank optimization by edge selection"
    - "learning equilibria of games via payoff queries"

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)