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: "the communicative multiagent team decision problem analyzing teamwork theories and models"
  Abstract: Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.

Connected through hyperedge HE-1 (induced by source paper "learning multi agent communication under limited bandwidth restriction for internet packet routing"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Connected through hyperedge HE-2 (induced by source paper "interactive ai with a theory of mind"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "machine teaching of active sequential learners"
    - "user modelling for avoiding overfitting in interactive knowledge elicitation for prediction"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "learning with opponent learning awareness"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "building machines that learn and think like people"

Connected through hyperedge HE-3 (induced by source paper "safe policy synthesis in multi agent pomdps via discrete time barrier functions"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "barrier certificates for assured machine teaching"
    - "privacy verification in pomdps via barrier certificates"
    - "shielded decision making in mdps"
    - "enhancing the performance of a safe controller via supervised learning for truck lateral control"

Connected through hyperedge HE-4 (induced by source paper "optimal decision making in mixed agent partially observable stochastic environments via reinforcement learning"):
  with members:
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"
    - "the complexity of decentralized control of markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "cm3 cooperative multi goal multi stage multi agent reinforcement learning"):
  with members:
    - "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a survey of inverse reinforcement learning challenges methods and progress"):
  with members:
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "learning to cooperate via policy search"
    - "multi agent inverse reinforcement learning for zero sum games"
    - "computational rationalization the inverse equilibrium problem"
    - "continuous inverse optimal control with locally optimal examples"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"
    - "a framework for sequential planning in multi agent settings"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "learning based physical layer communications for multi agent collaboration"):
  with members:
    - "a very brief introduction to machine learning with applications to communication systems"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"

Connected through hyperedge HE-8 (induced by source paper "on learning how to communicate over noisy channels for collaborative tasks"):
  with members:
    - "a very brief introduction to machine learning with applications to communication systems"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"

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.AI(Artificial Intelligence)