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: "measuring the size of large no limit poker games"
  Abstract: In the eld of computational game theory, games are often compared in terms of their size. This can be measured in several ways, including the number of unique game states, the number of decision points, and the total number of legal actions over all decision points. These numbers are either known or estimated for a wide range of classic games such as chess and checkers. In the stochastic and imperfect information game of poker, these sizes are easily computed in \limit" games which restrict the players’ available actions, but until now had only been estimated for the more complicated \no-limit" variants. In this paper, we describe a simple algorithm for quickly computing the size of two-player no-limit poker games, provide an implementation of this algorithm, and present for the rst time precise counts of the number of game states, information sets, actions and terminal nodes in the no-limit poker games played in the Annual Computer Poker Competition.

Connected through hyperedge HE-1 (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-2 (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"
    - "a generalised method for empirical game theoretic analysis"
    - "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"

Connected through hyperedge HE-3 (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"
    - "solving imperfect information games using decomposition"

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"
    - "a unified view of large scale zero sum equilibrium computation"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-5 (induced by source paper "analysis and optimization of deep counterfactual value networks"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "adam a method for stochastic optimization"
    - "solving large imperfect information games using cfr"
    - "solving imperfect information games using decomposition"

Connected through hyperedge HE-6 (induced by source paper "on the complexity of reconnaissance blind chess"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "solving imperfect information games using decomposition"

Connected through hyperedge HE-7 (induced by source paper "approximating poker probabilities with deep learning"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "solving large sequential games with the excessive gap technique"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "theoretical and practical advances on smoothing for extensive form games"

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)