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: "ms pac man versus ghost team cig 2016 competition"
  Abstract: This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost Team competition. We present an updated game engine with Partial Observability constraints, a new Multi-Agent Systems approach to developing Ghost agents and several sample controllers to ease the development of entries. A restricted communication protocol is provided for the Ghosts, providing a more challenging environment than before. The competition will debut at the IEEE Computational Intelligence and Games Conference 2016. Some preliminary results showing the effects of Partial Observability and the benefits of simple communication are also presented.

Connected through hyperedge HE-1 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "introducing the hearthstone ai competition"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-3 (induced by source paper "vizdoom competitions playing doom from pixels"):
  with members:
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "home a household multimodal environment"
    - "curiosity driven exploration by self supervised prediction"
    - "unrealcv connecting computer vision to unreal engine"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "playing doom with slam augmented deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-4 (induced by source paper "starcraft ii a new challenge for reinforcement learning"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "deep q learning from demonstrations"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "sim to real robot learning from pixels with progressive nets"

Connected through hyperedge HE-5 (induced by source paper "learning to teach reinforcement learning agents"):
  with members:
    - "online transfer learning in reinforcement learning domains"

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.AI(Artificial Intelligence)