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: "multi agent common knowledge reinforcement learning"
  Abstract: Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show that common knowledge between agents allows for complex decentralised coordination. Common knowledge arises naturally in a large number of decentralised cooperative multi-agent tasks, for example, when agents can reconstruct parts of each others' observations. Since agents an independently agree on their common knowledge, they can execute complex coordinated policies that condition on this knowledge in a fully decentralised fashion. We propose multi-agent common knowledge reinforcement learning (MACKRL), a novel stochastic actor-critic algorithm that learns a hierarchical policy tree. Higher levels in the hierarchy coordinate groups of agents by conditioning on their common knowledge, or delegate to lower levels with smaller subgroups but potentially richer common knowledge. The entire policy tree can be executed in a fully decentralised fashion. As the lowest policy tree level consists of independent policies for each agent, MACKRL reduces to independently learnt decentralised policies as a special case. We demonstrate that our method can exploit common knowledge for superior performance on complex decentralised coordination tasks, including a stochastic matrix game and challenging problems in StarCraft II unit micromanagement.

Connected through hyperedge HE-1 (induced by source paper "qatten a general framework for cooperative multiagent reinforcement learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "are transformers universal approximators of sequence to sequence functions"
    - "maven multi agent variational exploration"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "lenient multi agent deep reinforcement learning"
    - "attention is all you need"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-2 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "the starcraft multi agent challenge"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "value propagation networks"
    - "mean field multi agent reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-4 (induced by source paper "cesma centralized expert supervises multi agents"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "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"
    - "action branching architectures for deep reinforcement learning"
    - "value decomposition networks for cooperative multi agent learning"

Connected through hyperedge HE-5 (induced by source paper "deep coordination graphs"):
  with members:
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "the starcraft multi agent challenge"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "exploration with unreliable intrinsic reward in multi agent reinforcement learning"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multiagent soft q learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-6 (induced by source paper "improving policies via search in cooperative partially observable games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "distributed prioritized experience replay"
    - "evaluating and modelling hanabi playing agents"
    - "prioritized experience replay"

Connected through hyperedge HE-7 (induced by source paper "efficient communication in multi agent reinforcement learning via variance based control"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "counterfactual multi agent policy gradients"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "value decomposition networks for cooperative multi agent learning"

Connected through hyperedge HE-8 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system"):
  with members:
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "deep reinforcement learning for intelligent transportation systems"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "continuous control with deep reinforcement learning"
    - "multiagent cooperation and competition with deep 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)