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: "decentralized control of cooperative systems categorization and complexity analysis"
  Abstract: Decentralized control of cooperative systems captures the operation of a group of decision-makers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general problem has been shown to be NEXP-complete. In this paper, we identify classes of decentralized control problems whose complexity ranges between NEXP and P. In particular, we study problems characterized by independent transitions, independent observations, and goal-oriented objective functions. Two algorithms are shown to solve optimally useful classes of goal-oriented decentralized processes in polynomial time. This paper also studies information sharing among the decision-makers, which can improve their performance. We distinguish between three ways in which agents can exchange information: indirect communication, direct communication and sharing state features that are not controlled by the agents. Our analysis shows that for every class of problems we consider, introducing direct or indirect communication does not change the worst-case complexity. The results provide a better understanding of the complexity of decentralized control problems that arise in practice and facilitate the development of planning algorithms for these problems.

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 "message dropout an efficient training method for multi agent deep reinforcement learning"):
  with members:
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "analysis of dropout learning regarded as ensemble learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "design of a solver for multi agent epistemic planning"):
  with members:
    - "timeline based planning expressiveness and complexity"
    - "cooperative epistemic multi agent planning for implicit coordination"
    - "a general multi agent epistemic planner based on higher order belief change"
    - "the lama planner guiding cost based anytime planning with landmarks"

Connected through hyperedge HE-4 (induced by source paper "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "collaborative deep reinforcement learning for joint object search"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Connected through hyperedge HE-5 (induced by source paper "self fulfilling signal of an endogenous state in network congestion games"):
  with members:
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "multi agent path finding with delay probabilities"):
  with members:
    - "optimal target assignment and path finding for teams of agents"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

Connected through hyperedge HE-7 (induced by source paper "an action language for multi agent domains foundations"):
  with members:
    - "logic programming for finding models in the logics of knowledge and its applications a case study"
    - "reinforcement learning for adaptive routing"

Connected through hyperedge HE-8 (induced by source paper "multiagent conflict resolution for a specification network of discrete event coordinating agents"):
  with members:
    - "supervisor localization of discrete event systems based on state tree structures"

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)