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: "magent a many agent reinforcement learning platform for artificial collective intelligence"
  Abstract: We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents' optimal polices, but more importantly, the observation and understanding of individual agent's behaviors and social phenomena emerging from the AI society, including communication languages, leaderships, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. In this demo, we present three environments designed on MAgent and show emerged collective intelligence by learning from scratch.

Connected through hyperedge HE-1 (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"
    - "mean field multi agent reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "ray a distributed framework for emerging ai applications"
    - "attention is all you need"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "slm lab a comprehensive benchmark and modular software framework for reproducible deep reinforcement learning"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "rlgraph modular computation graphs for deep reinforcement learning"
    - "unity a general platform for intelligent agents"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "tune a research platform for distributed model selection and training"

Connected through hyperedge HE-4 (induced by source paper "efficient collaborative multi agent deep reinforcement learning for large scale fleet management"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "policy gradient with value function approximation for collective multiagent planning"
    - "counterfactual multi agent policy gradients"
    - "deep reinforcement learning a brief survey"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"
    - "value decomposition networks for cooperative multi agent learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-5 (induced by source paper "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "playing atari with six neurons"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"

Connected through hyperedge HE-6 (induced by source paper "multi agent reinforcement learning for order dispatching via order vehicle distribution matching"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "learning to communicate to solve riddles with deep distributed recurrent q networks"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-7 (induced by source paper "a regulation enforcement solution for multi agent reinforcement learning"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "qmix monotonic value function factorisation for deep multi agent reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "smix lambda enhancing centralized value functions for cooperative multi agent reinforcement learning"
    - "deep multi agent reinforcement learning with discrete continuous hybrid action spaces"
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "hierarchical cooperative multi agent reinforcement learning with skill discovery"
    - "learning nearly decomposable value functions via communication minimization"
    - "the starcraft multi agent challenge"
    - "deep coordination graphs"

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.LG(Machine Learning)