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: "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
  Abstract: Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of individual decision making frameworks. However, individual decision making in multiagent settings faces the task of having to reason about other agents' actions, which in turn involves reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. We show that a consequence of the finitely-nested modeling is that we may not obtain optimal team solutions in cooperative settings. We address this limitation by including models at level 0 whose solutions involve learning. We demonstrate that the learning integrated into planning in the context of interactive dynamic influence diagrams facilitates optimal team behavior, and is applicable to ad hoc teamwork.

Connected through hyperedge HE-1 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-2 (induced by source paper "teaching social behavior through human reinforcement for ad hoc teamwork the star framework"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "incorporating behavioral constraints in online ai systems"
    - "building ethically bounded ai"
    - "incomplete contracting and ai alignment"
    - "building ethics into artificial intelligence"
    - "inverse reward design"
    - "safe reinforcement learning via shielding"
    - "belief and truth in hypothesised behaviours"

Connected through hyperedge HE-3 (induced by source paper "learning sharing behaviors with arbitrary numbers of agents"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "belief and truth in hypothesised behaviours"
    - "playing atari with deep reinforcement learning"
    - "bayes bluff opponent modelling in poker"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "individual planning in agent populations exploiting anonymity and frame action hypergraphs"):
  with members:
    - "graphical models for game theory"
    - "context specific independence in bayesian networks"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "temporal action graph games a new representation for dynamic games"
    - "learning from humans as an i pomdp"
    - "a framework for sequential planning in multi agent settings"

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)