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: "leveraging statistical multi agent online planning with emergent value function approximation"
  Abstract: Making decisions is a great challenge in distributed autonomous environments due to enormous state spaces and uncertainty. Many online planning algorithms rely on statistical sampling to avoid searching the whole state space, while still being able to make acceptable decisions. However, planning often has to be performed under strict computational constraints making online planning in multi-agent systems highly limited, which could lead to poor system performance, especially in stochastic domains. In this paper, we propose Emergent Value function Approximation for Distributed Environments (EVADE), an approach to integrate global experience into multi-agent online planning in stochastic domains to consider global effects during local planning. For this purpose, a value function is approximated online based on the emergent system behaviour by using methods of reinforcement learning. We empirically evaluated EVADE with two statistical multi-agent online planning algorithms in a highly complex and stochastic smart factory environment, where multiple agents need to process various items at a shared set of machines. Our experiments show that EVADE can effectively improve the performance of multi-agent online planning while offering efficiency w.r.t. the breadth and depth of the planning process.

Connected through hyperedge HE-1 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies"):
  with members:
    - "open loop execution of tree search algorithms"
    - "machine theory of mind"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "memory bounded open loop planning in large pomdps using thompson sampling"):
  with members:
    - "open loop execution of tree search algorithms"
    - "stacked thompson bandits"
    - "further optimal regret bounds for thompson sampling"
    - "heuristic search value iteration for pomdps"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-3 (induced by source paper "the sharer s dilemma in collective adaptive systems of self interested agents"):
  with members:
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "distributed constraint optimization problems and applications a survey"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "machine theory of mind"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"

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)