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 conventions in multiagent stochastic domains using likelihood estimates"
  Abstract: Fully cooperative multiagent systems - those in which agents share a joint utility model- is of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where the agents are autonomous decision makers. We investigate approaches to learning coordinated strategies in stochastic domains where an agent's actions are not directly observable by others. Much recent work in game theory has adopted a Bayesian learning perspective to the more general problem of equilibrium selection, but tends to assume that actions can be observed. We discuss the special problems that arise when actions are not observable, including effects on rates of convergence, and the effect of action failure probabilities and asymmetries. We also use likelihood estimates as a means of generalizing fictitious play learning models in our setting. Finally, we propose the use of maximum likelihood as a means of removing strategies from consideration, with the aim of convergence to a conventional equilibrium, at which point learning and deliberation can cease.

Connected through hyperedge HE-1 (induced by source paper "adversarially guided self play for adopting social conventions"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "learning existing social conventions via observationally augmented self play"
    - "a style based generator architecture for generative adversarial networks"
    - "context aware captions from context agnostic supervision"
    - "emergence of grounded compositional language in multi agent populations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "multi agent actor critic for mixed cooperative competitive environments"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "an introduction to collective intelligence"):
  with members:
    - "avoiding braess paradox through collective intelligence"
    - "general principles of learning based multi agent systems"
    - "using collective intelligence to route internet traffic"
    - "anytime coalition structure generation with worst case guarantees"
    - "reinforcement learning a survey"
    - "adaptive load balancing a study in multi agent learning"
    - "a market oriented programming environment and its application to distributed multicommodity flow 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)