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: "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
  Abstract: Stackelberg equilibria have become increasingly important as a solution concept in computational game theory, largely inspired by practical problems such as security settings. In practice, however, there is typically uncertainty regarding the model about the opponent. This paper is, to our knowledge, the first to investigate Stackelberg equilibria under uncertainty in extensive-form games, one of the broadest classes of game. We introduce robust Stackelberg equilibria, where the uncertainty is about the opponent's payoffs, as well as ones where the opponent has limited lookahead and the uncertainty is about the opponent's node evaluation function. We develop a new mixed-integer program for the deterministic limited-lookahead setting. We then extend the program to the robust setting for Stackelberg equilibrium under unlimited and under limited lookahead by the opponent. We show that for the specific case of interval uncertainty about the opponent's payoffs (or about the opponent's node evaluations in the case of limited lookahead), robust Stackelberg equilibria can be computed with a mixed-integer program that is of the same asymptotic size as that for the deterministic setting.

Connected through hyperedge HE-1 (induced by source paper "stochastic regret minimization in extensive form games"):
  with members:
    - "a modern introduction to online learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "regret circuits composability of regret minimizers"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-2 (induced by source paper "limited lookahead in imperfect information games"):
  with members:
    - "depth limited solving for imperfect information games"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "extensive form game imperfect recall abstractions with bounds"
    - "no regret learning in extensive form games with imperfect recall"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-3 (induced by source paper "solving online threat screening games using constrained action space reinforcement learning"):
  with members:
    - "resource constrained deep reinforcement learning"
    - "deep reinforcement learning for green security games with real time information"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"
    - "optnet differentiable optimization as a layer in neural networks"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "leadership in singleton congestion games what is hard and what is easy"):
  with members:
    - "quasi perfect stackelberg equilibrium"
    - "computing the strategy to commit to in polymatrix games extended version"
    - "computing a pessimistic leader follower equilibrium with multiple followers the mixed pure case"
    - "protection of flows under targeted attacks"
    - "generalized mirror descents in congestion games"
    - "multidefender security games"

Connected through hyperedge HE-5 (induced by source paper "quasi perfect stackelberg equilibrium"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "regret minimization in behaviorally constrained zero sum games"
    - "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)