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 large extensive form games with strategy constraints"
  Abstract: Extensive-form games are a common model for multiagent interactions with imperfect information. In two-player zero-sum games, the typical solution concept is a Nash equilibrium over the unconstrained strategy set for each player. In many situations, however, we would like to constrain the set of possible strategies. For example, constraints are a natural way to model limited resources, risk mitigation, safety, consistency with past observations of behavior, or other secondary objectives for an agent. In small games, optimal strategies under linear constraints can be found by solving a linear program; however, state-of-the-art algorithms for solving large games cannot handle general constraints. In this work we introduce a generalized form of Counterfactual Regret Minimization that provably finds optimal strategies under any feasible set of convex constraints. We demonstrate the effectiveness of our algorithm for finding strategies that mitigate risk in security games, and for opponent modeling in poker games when given only partial observations of private information.

Connected through hyperedge HE-1 (induced by source paper "efficient regret minimization algorithm for extensive form correlated equilibrium"):
  with members:
    - "introduction to online convex optimization"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"
    - "extensive form perfect equilibrium computation in two player 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)