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: "depth limited solving for imperfect information games"
  Abstract: A fundamental challenge in imperfect-information games is that states do not have well-defined values. As a result, depth-limited search algorithms used in single-agent settings and perfect-information games do not apply. This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit. Each one of these strategies results in a different set of values for leaf nodes. This forces an agent to be robust to the different strategies an opponent may employ. We demonstrate the effectiveness of this approach by building a master-level heads-up no-limit Texas hold'em poker AI that defeats two prior top agents using only a 4-core CPU and 16 GB of memory. Developing such a powerful agent would have previously required a supercomputer.

Connected through hyperedge HE-1 (induced by source paper "single deep counterfactual regret minimization"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization for partially observable deep reinforcement learning"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-2 (induced by source paper "low variance and zero variance baselines for extensive form games"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "aivat a new variance reduction technique for agent evaluation in imperfect information games"

Connected through hyperedge HE-3 (induced by source paper "optimistic regret minimization for extensive form games via dilated distance generating functions"):
  with members:
    - "introduction to online convex optimization"
    - "stable predictive optimistic counterfactual regret minimization"
    - "first order methods with increasing iterate averaging for solving saddle point problems"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-4 (induced by source paper "stable predictive optimistic counterfactual regret minimization"):
  with members:
    - "regret circuits composability of regret minimizers"
    - "acceleration through optimistic no regret dynamics"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-5 (induced by source paper "limited lookahead in imperfect information games"):
  with members:
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "extensive form game imperfect recall abstractions with bounds"
    - "no regret learning in extensive form games with imperfect recall"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "value functions for depth limited solving in imperfect information games beyond poker"):
  with members:
    - "rethinking formal models of partially observable multiagent decision making"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "solving imperfect information games using decomposition"

Connected through hyperedge HE-7 (induced by source paper "building a computer mahjong player via deep convolutional neural networks"):
  with members:
    - "safe and nested subgame solving for imperfect information games"

Connected through hyperedge HE-8 (induced by source paper "deep counterfactual regret minimization"):
  with members:
    - "single deep counterfactual regret minimization"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "double neural counterfactual regret minimization"

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)