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: "computational rationalization the inverse equilibrium problem"
  Abstract: Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that explains the example behavior and can then be used to accurately predict or imitate future behavior in similar observed or unobserved situations. #R##N#In this work, we consider similar tasks in competitive and cooperative multi-agent domains. Here, unlike single-agent settings, a player cannot myopically maximize its reward; it must speculate on how the other agents may act to influence the game's outcome. Employing the game-theoretic notion of regret and the principle of maximum entropy, we introduce a technique for predicting and generalizing behavior.

Connected through hyperedge HE-1 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"

Connected through hyperedge HE-2 (induced by source paper "multi agent adversarial inverse reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "coordinated multi agent imitation learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-3 (induced by source paper "strategic prediction with latent aggregative games"):
  with members:
    - "the mechanics of n player differentiable games"
    - "learning to speed up structured output prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "learning with opponent learning awareness"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "on structured prediction theory with calibrated convex surrogate losses"

Connected through hyperedge HE-4 (induced by source paper "modeling and optimization of human machine interaction processes via the maximum entropy principle"):
  with members:
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "continuum armed bandit problem of few variables in high dimensions"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "large scale learning of agent rationality in two player zero sum games"):
  with members:
    - "what game are we playing end to end learning in normal and extensive form games"
    - "theoretical and practical advances on smoothing for extensive form games"
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"

Connected through hyperedge HE-6 (induced by source paper "robust commitments and partial reputation"):
  with members:
    - "persuasion with limited communication"
    - "algorithmic bayesian persuasion"
    - "constrained signaling in auction design"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-7 (induced by source paper "a survey of inverse reinforcement learning challenges methods and progress"):
  with members:
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "learning to cooperate via policy search"
    - "multi agent inverse reinforcement learning for zero sum games"
    - "continuous inverse optimal control with locally optimal examples"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"
    - "a framework for sequential planning in multi agent settings"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"
    - "reinforcement learning a survey"

Connected through hyperedge HE-8 (induced by source paper "multi agent inverse reinforcement learning for general sum stochastic games"):
  with members:
    - "cooperative inverse reinforcement learning"
    - "decentralized anti coordination through multi agent learning"
    - "inverse reinforcement learning with gaussian process"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"
    - "awesome a general multiagent learning algorithm that converges in self play and learns a best response against stationary opponents"

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)