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 unified view of large scale zero sum equilibrium computation"
  Abstract: The task of computing approximate Nash equilibria in large zero-sum extensive-form games has received a tremendous amount of attention due mainly to the Annual Computer Poker Competition. Immediately after its inception, two competing and seemingly different approaches emerged---one an application of no-regret online learning, the other a sophisticated gradient method applied to a convex-concave saddle-point formulation. Since then, both approaches have grown in relative isolation with advancements on one side not effecting the other. In this paper, we rectify this by dissecting and, in a sense, unify the two views.

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 "towards understanding chinese checkers with heuristics monte carlo tree search and deep reinforcement learning"):
  with members:
    - "robust optimization for non convex objectives"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "playing fps games with deep reinforcement learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "solving zero sum extensive form games with arbitrary payoff uncertainty models"):
  with members:
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"
    - "double neural counterfactual regret minimization"
    - "regret minimization in non zero sum games with applications to building champion multiplayer computer poker agents"

Connected through hyperedge HE-4 (induced by source paper "solving large extensive form games with strategy constraints"):
  with members:
    - "theoretical and practical advances on smoothing for extensive form games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "measuring the size of large no limit poker games"
    - "bayes bluff opponent modelling in poker"

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)