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: "accelerating self play learning in go"
  Abstract: By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carlo tree search self-play. But whereas AlphaZero required thousands of TPUs over several days and ELF required thousands of GPUs over two weeks, KataGo surpasses ELF's final model after only 19 days on fewer than 30 GPUs. Much of the speedup involves non-domain-specific improvements that might directly transfer to other problems. Further gains from domain-specific techniques reveal the remaining efficiency gap between the best methods and purely general methods such as AlphaZero. Our work is a step towards making learning in state spaces as large as Go possible without large-scale computational resources.

Connected through hyperedge HE-1 (induced by source paper "accelerating and improving alphazero using population based training"):
  with members:
    - "hyper parameter sweep on alphazero general"
    - "openspiel a framework for reinforcement learning in games"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "population based training of neural networks"
    - "better computer go player with neural network and long term prediction"

Connected through hyperedge HE-2 (induced by source paper "polygames improved zero learning"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "on the complexity of connection games"

Connected through hyperedge HE-3 (induced by source paper "self play learning without a reward metric"):
  with members:
    - "deep ordinal reinforcement learning"
    - "sai a sensible artificial intelligence that plays with handicap and targets high scores in 9x9 go extended version"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning values across many orders of magnitude"

Connected through hyperedge HE-4 (induced by source paper "sai a sensible artificial intelligence that plays with handicap and targets high scores in 9x9 go extended version"):
  with members:
    - "sai a sensible artificial intelligence that plays go"
    - "learning to play othello with deep neural networks"
    - "multi labelled value networks for computer go"

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)