human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a model of human cooperation in social dilemmas":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a game theoretic analysis of the social impact of connected and automated vehicles")
  Member "graphical models for game theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "learning game representations from data using rationality constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a robot that learns connect four using game theory and demonstrations")

Question: Please tell me which class the source paper 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)