human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural mmo v1 3 a massively multiagent game environment for training and evaluating neural networks"):
  Abstract: Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targeted and demonstrated success on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting.

Includes members:
  Member "emergent tool use from multi agent autocurricula":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "neural mmo a massively multiagent game environment for training and evaluating intelligent agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tanksworld a multi agent environment for ai safety research")
  Member "human level performance in 3d multiplayer games with population based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "mean field multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph")
  Member "gotta learn fast a new benchmark for generalization in rl":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "magent a many agent reinforcement learning platform for artificial collective intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the starcraft multi agent challenge")
  Member "ray a distributed framework for emerging ai applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)