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 nash equilibria and robust best responses using sampling"
  Abstract: This article discusses two contributions to decision-making in complex partially observable stochastic games. First, we apply two state-of-the-art search techniques that use Monte-Carlo sampling to the task of approximating a Nash-Equilibrium (NE) in such games, namely Monte-Carlo Tree Search (MCTS) and Monte-Carlo Counterfactual Regret Minimization (MCCFR). MCTS has been proven to approximate a NE in perfect-information games. We show that the algorithm quickly finds a reasonably strong strategy (but not a NE) in a complex imperfect information game, i.e. Poker. MCCFR on the other hand has theoretical NE convergence guarantees in such a game. We apply MCCFR for the first time in Poker. Based on our experiments, we may conclude that MCTS is a valid approach if one wants to learn reasonably strong strategies fast, whereas MCCFR is the better choice if the quality of the strategy is most important.#R##N##R##N#Our second contribution relates to the observation that a NE is not a best response against players that are not playing a NE. We present Monte-Carlo Restricted Nash Response (MCRNR), a sample-based algorithm for the computation of restricted Nash strategies. These are robust bestresponse strategies that (1) exploit non-NE opponents more than playing a NE and (2) are not (overly) exploitable by other strategies. We combine the advantages of two state-of-the-art algorithms, i.e. MCCFR and Restricted Nash Response (RNR). MCRNR samples only relevant parts of the game tree. We show that MCRNR learns quicker than standard RNR in smaller games. Also we show in Poker that MCRNR learns robust best-response strategies fast, and that these strategies exploit opponents more than playing a NE does.

Connected through hyperedge HE-1 (induced by source paper "survey of artificial intelligence for card games and its application to the swiss game jass"):
  with members:
    - "improving search with supervised learning in trick based card games"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "deep counterfactual regret minimization"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "application of self play reinforcement learning to a four player game of imperfect information"
    - "traditional wisdom and monte carlo tree search face to face in the card game scopone"
    - "evaluating and modelling hanabi playing agents"

Connected through hyperedge HE-2 (induced by source paper "combining prediction of human decisions with ismcts in imperfect information games"):
  with members:
    - "opponent modeling in deep reinforcement learning"
    - "deep learning in neural networks"
    - "gib imperfect information in a computationally challenging game"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning from self play in imperfect information games"):
  with members:
    - "the predictron end to end learning and planning"
    - "deep learning in neural networks"
    - "continuous control with deep reinforcement learning"
    - "move evaluation in go using deep convolutional neural networks"
    - "teaching deep convolutional neural networks to play go"
    - "solving games with functional regret estimation"
    - "solving imperfect information games using decomposition"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-4 (induced by source paper "analysis of hannan consistent selection for monte carlo tree search in simultaneous move games"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "bandit algorithms for tree search"
    - "convergence of monte carlo tree search in simultaneous move games"
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"
    - "computer solution to the game of pure strategy"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-5 (induced by source paper "on sparse discretization for graphical games"):
  with members:
    - "graphical models for game theory"
    - "interdependent defense games modeling interdependent security under deliberate attacks"
    - "discretized multinomial distributions and nash equilibria in anonymous games"
    - "settling the complexity of computing two player nash equilibria"
    - "computing nash equilibria approximation and smoothed complexity"
    - "pure nash equilibria hard and easy games"
    - "a new look at survey propagation and its generalizations"
    - "constraint optimization and statistical mechanics"

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)