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: "the multi agent reinforcement learning in malmo marlo competition"
  Abstract: Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence.

Connected through hyperedge HE-1 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors a retrospective"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "deep reinforcement learning from policy dependent human feedback"
    - "hierarchical deep q network with forgetting from imperfect demonstrations in minecraft"
    - "string diagrams for assembly planning"
    - "improving deep reinforcement learning in minecraft with action advice"
    - "keeping your distance solving sparse reward tasks using self balancing shaped rewards"
    - "obstacle tower a generalization challenge in vision control and planning"

Connected through hyperedge HE-2 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors"):
  with members:
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
    - "generative design in minecraft gdmc settlement generation competition"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "rainbow combining improvements in deep reinforcement learning"
    - "one shot visual imitation learning via meta learning"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-3 (induced by source paper "rinascimento optimising statistical forward planning agents for playing splendor"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "generative design in minecraft gdmc settlement generation competition"
    - "population seeding techniques for rolling horizon evolution in general video game playing"
    - "playing atari with deep reinforcement learning"

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)