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: "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
  Abstract: The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs), that aims to simulate this setting in microcosm. As with MMORPGs and the real world alike, our environment is persistent and supports a large and variable number of agents. Our environment is well suited to the study of large-scale multiagent interaction: it requires that agents learn robust combat and navigation policies in the presence of large populations attempting to do the same. Baseline experiments reveal that population size magnifies and incentivizes the development of skillful behaviors and results in agents that outcompete agents trained in smaller populations. We further show that the policies of agents with unshared weights naturally diverge to fill different niches in order to avoid competition.

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"
    - "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"
    - "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-2 (induced by source paper "tanksworld a multi agent environment for ai safety research"):
  with members:
    - "benchmarking batch deep reinforcement learning algorithms"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "on the complexity of reconnaissance blind chess"
    - "ai safety gridworlds"
    - "dart noise injection for robust imitation learning"
    - "concrete problems in ai safety"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence"):
  with members:
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "open ended learning in symmetric zero sum games"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "action semantics network considering the effects of actions in multiagent systems"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"
    - "learning with opponent learning awareness"
    - "proximal policy optimization algorithms"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-5 (induced by source paper "rlcard a toolkit for reinforcement learning in card games"):
  with members:
    - "openspiel a framework for reinforcement learning in games"
    - "deep counterfactual regret minimization"
    - "unity a general platform for intelligent agents"
    - "virtual taobao virtualizing real world online retail environment for reinforcement learning"
    - "safe and nested subgame solving for imperfect information games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "towards a framework for the evolution of artificial general intelligence"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "evolutionary innovations and where to find them routes to open ended evolution in natural and artificial systems"
    - "pathnet evolution channels gradient descent in super neural networks"

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)