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 "learning implicit communication strategies for the purpose of illicit collusion"):
  Abstract: Winner-take-all dynamics are prevalent throughout the human and natural worlds. Such dynamics promote competition between agents as they secure available resources for themselves and undermine their opponents. However, this competition is mitigated by the capacity of any single agent to amass resources on its own. Thus, agents collude with one another to ensure that one of their own can win and distribute the spoils amongst the co-conspirators. #R##N#Such collusion can be difficult to execute successfully, and explicit collusion is often prohibited. Thus, competitors may attempt to collude by encoding signals in their publicly observable behavior. Such collusion happens in domains such as economics in which colluding agents establish cartels, in politics via the spoils system, and in biology where symbiotic systems can be more efficient than organisms acting alone. #R##N#However, it is not known by what mechanisms agents can establish such implicit collusion strategies. In this work, we do so by extending iterated prisoner's dilemma (IPD) into a winner-take-all (WTA) framework, formalize a WTA IPD strategy as a Markov decision process, and use classical and deep reinforcement learning to discover collusion strategies in WTA IPD. We then analyze the techniques that are learned to understand how agents can develop signaling mechanisms over restricted communication channels.

Includes members:
  Member "multi agent actor critic for mixed cooperative competitive environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "empirically evaluating multiagent learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stmarl a spatio temporal multi agent reinforcement learning approach for cooperative traffic light control")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)