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: "mean field multi agent reinforcement learning"
  Abstract: Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of user interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated by those between a single agent and the average effect from the overall population or neighboring agents; the interplay between the two entities is mutually reinforced: the learning of the individual agent's optimal policy depends on the dynamics of the population, while the dynamics of the population change according to the collective patterns of the individual policies. We develop practical mean field Q-learning and mean field Actor-Critic algorithms and analyze the convergence of the solution. Experiments on resource allocation, Ising model estimation, and battle game tasks verify the learning effectiveness of our mean field approaches in handling many-agent interactions in population.

Connected through hyperedge HE-1 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph"):
  with members:
    - "adaptively sparse transformers"
    - "learning transferable cooperative behavior in multi agent teams"
    - "graph policy gradients for large scale robot control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "on controllable sparse alternatives to softmax"

Connected through hyperedge HE-2 (induced by source paper "gamma reward a novel multi agent reinforcement learning method for traffic signal control"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "a survey on traffic signal control methods"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "colight learning network level cooperation for traffic signal control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "ray a distributed framework for emerging ai applications"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "neural mmo v1 3 a massively multiagent game environment for training and evaluating neural networks"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "ray a distributed framework for emerging ai applications"
    - "attention is all you need"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-4 (induced by source paper "represented value function approach for large scale multi agent reinforcement learning"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "stability properties of graph neural networks"
    - "concurrent meta reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-5 (induced by source paper "adaptive online distributed optimal control of very large scale robotic systems"):
  with members:
    - "represented value function approach for large scale multi agent reinforcement learning"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "deep reinforcement learning for swarm systems"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"

Connected through hyperedge HE-6 (induced by source paper "badger learning to learn learning algorithms through multi agent communication"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "efficient optimization of loops and limits with randomized telescoping sums"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning of sequential strategies"
    - "modularity as a means for complexity management in neural networks learning"
    - "a mathematical theory of cooperative communication"
    - "learning to communicate in multi agent reinforcement learning a review"
    - "mastering atari go chess and shogi by planning with a learned model"

Connected through hyperedge HE-7 (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"
    - "multi agent common knowledge reinforcement learning"
    - "value propagation networks"
    - "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-8 (induced by source paper "natural actor critic converges globally for hierarchical linear quadratic regulator"):
  with members:
    - "deep reinforcement learning for event driven multi agent decision processes"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "fully decentralized multi agent reinforcement learning with networked agents"

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)