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: "predicting human behavior in unrepeated simultaneous move games"
  Abstract: Abstract   It is commonly assumed that agents will adopt Nash equilibrium strategies; however, experimental studies have demonstrated that this is often a poor description of human players' behavior in unrepeated normal-form games. We analyze five widely studied models of human behavior: Quantal Response Equilibrium, Level- k , Cognitive Hierarchy, QLk, and Noisy Introspection. We performed what we believe is the most comprehensive meta-analysis of these models, leveraging ten datasets from the literature recording human play of two-player games. We first evaluated  predictive  performance, asking how well each model fits unseen  test data  using parameters calibrated from separate  training data . The QLk model ( Stahl and Wilson, 1994 ) consistently achieved the best performance. Using a Bayesian analysis, we found that QLk's estimated parameter values were not consistent with their intended economic interpretations. Finally, we evaluated model variants similar to QLk, identifying one ( Camerer et al., 2016 ) that achieves better predictive performance with fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-2 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-3 (induced by source paper "decision focused learning of adversary behavior in security games"):
  with members:
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"
    - "the mysteries of security games equilibrium computation becomes combinatorial algorithm design"
    - "adversarial patrolling with spatially uncertain alarm signals"
    - "learning adversary behavior in security games a pac model perspective"

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)