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: "regret minimization in non zero sum games with applications to building champion multiplayer computer poker agents"
  Abstract: In two-player zero-sum games, if both players minimize their average external regret, then the average of the strategy profiles converges to a Nash equilibrium. For n-player general-sum games, however, theoretical guarantees for regret minimization are less understood. Nonetheless, Counterfactual Regret Minimization (CFR), a popular regret minimization algorithm for extensive-form games, has generated winning three-player Texas Hold'em agents in the Annual Computer Poker Competition (ACPC). In this paper, we provide the first set of theoretical properties for regret minimization algorithms in non-zero-sum games by proving that solutions eliminate iterative strict domination. We formally define \emph{dominated actions} in extensive-form games, show that CFR avoids iteratively strictly dominated actions and strategies, and demonstrate that removing iteratively dominated actions is enough to win a mock tournament in a small poker game. In addition, for two-player non-zero-sum games, we bound the worst case performance and show that in practice, regret minimization can yield strategies very close to equilibrium. Our theoretical advancements lead us to a new modification of CFR for games with more than two players that is more efficient and may be used to generate stronger strategies than previously possible. Furthermore, we present a new three-player Texas Hold'em poker agent that was built using CFR and a novel game decomposition method. Our new agent wins the three-player events of the 2012 ACPC and defeats the winning three-player programs from previous competitions while requiring less resources to generate than the 2011 winner. Finally, we show that our CFR modification computes a strategy of equal quality to our new agent in a quarter of the time of standard CFR using half the memory.

Connected through hyperedge HE-1 (induced by source paper "learning to correlate in multi player general sum sequential games"):
  with members:
    - "computing optimal coarse correlated equilibria in sequential games"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "finding any nontrivial coarse correlated equilibrium is hard"

Connected through hyperedge HE-2 (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"
    - "a unified view of large scale zero sum equilibrium computation"

Connected through hyperedge HE-3 (induced by source paper "actor critic policy optimization in partially observable multiagent environments"):
  with members:
    - "combining no regret and q learning"
    - "introduction to online convex optimization"
    - "policy gradient with value function approximation for collective multiagent planning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "a generalised method for empirical game theoretic analysis"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a unified game theoretic approach to multiagent reinforcement learning"):
  with members:
    - "emergent complexity via multi agent competition"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "opponent modeling in deep reinforcement learning"
    - "using reinforcement learning to validate empirical game theoretic analysis a continuous double auction study"
    - "learning multiagent communication with backpropagation"
    - "multi agent cooperation and the emergence of natural language"
    - "action conditional video prediction using deep networks in atari 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)