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: "solving imperfect information games using decomposition"
  Abstract: Decomposition, i.e. independently analyzing possible subgames, has proven to be an essential principle for effective decision-making in perfect information games. However, in imperfect information games, decomposition has proven to be problematic. To date, all proposed techniques for decomposition in imperfect information games have abandoned theoretical guarantees. This work presents the first technique for decomposing an imperfect information game into subgames that can be solved independently, while retaining optimality guarantees on the full-game solution. We can use this technique to construct theoretically justified algorithms that make better use of information available at run-time, overcome memory or disk limitations at run-time, or make a time/space trade-off to overcome memory or disk limitations while solving a game. In particular, we present an algorithm for subgame solving which guarantees performance in the whole game, in contrast to existing methods which may have unbounded error. In addition, we present an offline game solving algorithm, CFR-D, which can produce a Nash equilibrium for a game that is larger than available storage.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

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"
    - "depth limited solving for imperfect information games"
    - "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"

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"
    - "depth limited solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"

Connected through hyperedge HE-4 (induced by source paper "rethinking formal models of partially observable multiagent decision making"):
  with members:
    - "problems with the efg formalism a solution attempt using observations"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "avoiding wireheading with value reinforcement learning"
    - "timeability of extensive form games"
    - "learning to cooperate via policy search"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-5 (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"
    - "depth limited solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "most important fundamental rule of poker strategy"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "measuring the size of large no limit poker games"

Connected through hyperedge HE-7 (induced by source paper "problems with the efg formalism a solution attempt using observations"):
  with members:
    - "rethinking formal models of partially observable multiagent decision making"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-8 (induced by source paper "regret circuits composability of regret minimizers"):
  with members:
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "solving large extensive form games with strategy constraints"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "deepstack expert level artificial intelligence in heads up no limit 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.GT(Computer Science and Game Theory)