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: "extensive form perfect equilibrium computation in two player games"
  Abstract: We study the problem of computing an Extensive-Form Perfect Equilibrium (EFPE) in 2-player games. This equilibrium concept refines the Nash equilibrium requiring resilience w.r.t. a specific vanishing perturbation (representing mistakes of the players at each decision node). The scientific challenge is intrinsic to the EFPE definition: it requires a perturbation over the agent form, but the agent form is computationally inefficient, due to the presence of highly nonlinear constraints. We show that the sequence form can be exploited in a non-trivial way and that, for general-sum games, finding an EFPE is equivalent to solving a suitably perturbed linear complementarity problem. We prove that Lemke's algorithm can be applied, showing that computing an EFPE is $\textsf{PPAD}$-complete. In the notable case of zero-sum games, the problem is in $\textsf{FP}$ and can be solved by linear programming. Our algorithms also allow one to find a Nash equilibrium when players cannot perfectly control their moves, being subject to a given execution uncertainty, as is the case in most realistic physical settings.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "online convex optimization for sequential decision processes and extensive form games"):
  with members:
    - "introduction to online convex optimization"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "regret minimization for partially observable deep reinforcement learning"
    - "theoretical and practical advances on smoothing for extensive form games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"

Connected through hyperedge HE-3 (induced by source paper "quasi perfect stackelberg equilibrium"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "regret minimization in behaviorally constrained zero sum games"

Connected through hyperedge HE-4 (induced by source paper "regret minimization in behaviorally constrained zero sum games"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "theoretical and practical advances on smoothing for extensive form games"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"
    - "bayes bluff opponent modelling in poker"
    - "blackwell approachability and minimax theory"

Connected through hyperedge HE-5 (induced by source paper "smoothing method for approximate extensive form perfect equilibrium"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "bayes bluff opponent modelling in 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)