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: "computing approximate equilibria in sequential adversarial games by exploitability descent"
  Abstract: In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents. We prove that when following this optimization, the exploitability of a player's strategy converges asymptotically to zero, and hence when both players employ this optimization, the joint policies converge to a Nash equilibrium. Unlike fictitious play (XFP) and counterfactual regret minimization (CFR), our convergence result pertains to the policies being optimized rather than the average policies. Our experiments demonstrate convergence rates comparable to XFP and CFR in four benchmark games in the tabular case. Using function approximation, we find that our algorithm outperforms the tabular version in two of the games, which, to the best of our knowledge, is the first such result in imperfect information games among this class of algorithms.

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"
    - "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"
    - "negative momentum for improved game dynamics"

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"
    - "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"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"

Connected through hyperedge HE-3 (induced by source paper "neural replicator dynamics"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "alpha rank multi agent evaluation by evolution"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-4 (induced by source paper "improving fictitious play reinforcement learning with expanding models"):
  with members:
    - "neural fictitious self play on elf mini rts"
    - "openspiel a framework for reinforcement learning in games"
    - "open ended learning in symmetric zero sum games"
    - "deep counterfactual regret minimization"
    - "balancing two player stochastic games with soft q learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-5 (induced by source paper "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"):
  with members:
    - "single deep counterfactual regret minimization"
    - "openspiel a framework for reinforcement learning in games"
    - "neural replicator dynamics"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "deep counterfactual regret minimization"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-6 (induced by source paper "bounds for approximate regret matching algorithms"):
  with members:
    - "single deep counterfactual regret minimization"
    - "neural replicator dynamics"
    - "regret circuits composability of regret minimizers"
    - "deep counterfactual regret minimization"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "solving games with functional regret estimation"

Connected through hyperedge HE-7 (induced by source paper "the alpha mu search algorithm for the game of bridge"):
  with members:
    - "gib imperfect information in a computationally challenging game"

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.AI(Artificial Intelligence)