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: "an open framework for the reproducible study of the iterated prisoner s dilemma"
  Abstract: The Axelrod library is an open source Python package that allows for reproducible game theoretic research into the Iterated Prisoner’s Dilemma. This area of research began in the 1980s but suffers from a lack of documentation and test code. The goal of the library is to provide such a resource, with facilities for the design of new strategies and interactions between them, as well as conducting tournaments and ecological simulations for populations of strategies.  With a growing collection of 139 strategies, the library is a also a platform for an original tournament that, in itself, is of interest to the game theoretic community.  This paper describes the Iterated Prisoner’s Dilemma, the Axelrod library and its development, and insights gained from some novel research.

Connected through hyperedge HE-1 (induced by source paper "learning through probing a decentralized reinforcement learning architecture for social dilemmas"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "prioritized experience replay"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning produces dominant strategies for the iterated prisoner s dilemma"):
  with members:
    - "the numpy array a structure for efficient numerical computation"
    - "on some winning strategies for the iterated prisoner s dilemma or mr nice guy and the cosa nostra"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "invincible strategies of iterated prisoner s dilemma"):
  with members:
    - "a novel analytical method for evolutionary graph theory problems"

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)