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: "learning game representations from data using rationality constraints"
  Abstract: While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representations from data. If one wants to learn both the payoffs and the players' strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main contribution of this paper is to make this connection while learning. We formulate the learning problem as a weighted constraint satisfaction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the payoffs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporating rationality constraints can improve learning when the amount of data is limited.

Connected through hyperedge HE-1 (induced by source paper "a robot that learns connect four using game theory and demonstrations"):
  with members:
    - "few shot learning with meta metric learners"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "meta learning for semi supervised few shot classification"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-2 (induced by source paper "predicting human behavior in unrepeated simultaneous move games"):
  with members:
    - "a model of human cooperation in social dilemmas"
    - "graphical models for game theory"

Connected through hyperedge HE-3 (induced by source paper "learning the structure and parameters of large population graphical games from behavioral data"):
  with members:
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "maximum likelihood bounded tree width markov networks"
    - "graphical models for game theory"
    - "computational rationalization the inverse equilibrium problem"
    - "learning and solving many player games through a cluster based representation"
    - "convex structure learning for bayesian networks polynomial feature selection and approximate ordering"
    - "knowledge combination in graphical multiagent model"
    - "polynomial time computation of exact correlated equilibrium in compact 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)