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: "elf opengo an analysis and open reimplementation of alphazero"
  Abstract: The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available.

Connected through hyperedge HE-1 (induced by source paper "a hierarchy of limitations in machine learning"):
  with members:
    - "case study predictive fairness to reduce misdemeanor recidivism through social service interventions"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "the human body is a black box supporting clinical decision making with deep learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "an open source automl benchmark"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "roles for computing in social change"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"

Connected through hyperedge HE-2 (induced by source paper "solving np hard problems on graphs with extended alphago zero"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "accelerating and improving alphazero using population based training"):
  with members:
    - "accelerating self play learning in go"
    - "hyper parameter sweep on alphazero general"
    - "openspiel a framework for reinforcement learning in games"
    - "population based training of neural networks"
    - "better computer go player with neural network and long term prediction"

Connected through hyperedge HE-4 (induced by source paper "decentralized distributed ppo solving pointgoal navigation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "on evaluation of embodied navigation agents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "dd ppo learning near perfect pointgoal navigators from 2 5 billion frames"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "on evaluation of embodied navigation agents"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "the scientific method in the science of machine learning"):
  with members:
    - "model cards for model reporting"
    - "avoiding a tragedy of the commons in the peer review process"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "deterministic implementations for reproducibility in deep reinforcement learning"
    - "natural environment benchmarks for reinforcement learning"
    - "datasheets for datasets"
    - "distilling information from a flood a possibility for the use of meta analysis and systematic review in machine learning research"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"

Connected through hyperedge HE-7 (induced by source paper "slm lab a comprehensive benchmark and modular software framework for reproducible deep reinforcement learning"):
  with members:
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "rlgraph modular computation graphs for deep reinforcement learning"
    - "unity a general platform for intelligent agents"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "tune a research platform for distributed model selection and training"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-8 (induced by source paper "accelerating self play learning in go"):
  with members:
    - "sai a sensible artificial intelligence that plays go"
    - "sai a sensible artificial intelligence that plays with handicap and targets high scores in 9x9 go extended version"
    - "averaging weights leads to wider optima and better generalization"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "squeeze and excitation networks"
    - "a survey on multi task learning"
    - "multi labelled value networks for computer go"
    - "identity mappings in deep residual 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.AI(Artificial Intelligence)