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: "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
  Abstract: Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a best response during interactions accordingly. Previous works usually assume an opponent uses a stationary strategy or randomly switches among several stationary ones. However, in practice, an opponent may exhibit more sophisticated behaviors by adopting more advanced strategies, e.g., using a Bayesian reasoning strategy. This paper presents a novel algorithm called Bayes-ToMoP which can efficiently detect and handle opponents using either stationary or higher-level reasoning strategies. Bayes-ToMoP also supports the detection of previously unseen policies and learning a best-response policy accordingly. We provide theoretical guarantees of the optimal detection of the opponent's strategies. Deep Bayes-ToMoP is proposed by extending Bayes-ToMoP with DRL techniques. Experimental results show both Bayes-ToMoP and deep Bayes-ToMoP outperform the state-of-the-art approaches when faced with different types of opponents in two-agent competitive games.

Connected through hyperedge HE-1 (induced by source paper "on memory mechanism in multi agent reinforcement learning"):
  with members:
    - "r maddpg for partially observable environments and limited communication"
    - "on the pitfalls of measuring emergent communication"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "machine theory of mind"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-2 (induced by source paper "building second order mental models for human robot interaction"):
  with members:
    - "on the utility of model learning in hri"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "modelling user s theory of ai s mind in interactive intelligent systems"
    - "machine theory of mind"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "towards efficient detection and optimal response against sophisticated opponents"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "enabling robots to communicate their objectives"

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.MA(Multiagent Systems)