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: "better computer go player with neural network and long term prediction"
  Abstract: Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go's high branching factor makes traditional search techniques ineffective, even on leading-edge hardware, and Go's evaluation function could change drastically with one stone change. Recent works [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not strictly necessary for machine Go players. A pure pattern-matching approach, based on a Deep Convolutional Neural Network (DCNN) that predicts the next move, can perform as well as Monte Carlo Tree Search (MCTS)-based open source Go engines such as Pachi [Baudis & Gailly (2012)] if its search budget is limited. We extend this idea in our bot named darkforest, which relies on a DCNN designed for long-term predictions. Darkforest substantially improves the win rate for pattern-matching approaches against MCTS-based approaches, even with looser search budgets. Against human players, the newest versions, darkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a substantial improvement upon the estimated 4k-5k ranks for DCNN reported in Clark & Storkey (2015) based on games against other machine players. Adding MCTS to darkfores2 creates a much stronger player named darkfmcts3: with 5000 rollouts, it beats Pachi with 10k rollouts in all 250 games; with 75k rollouts it achieves a stable 5d level in KGS server, on par with state-of-the-art Go AIs (e.g., Zen, DolBaram, CrazyStone) except for AlphaGo [Silver et al. (2016)]; with 110k rollouts, it won the 3rd place in January KGS Go Tournament.

Connected through hyperedge HE-1 (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"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "population based training of neural networks"

Connected through hyperedge HE-2 (induced by source paper "luck matters understanding training dynamics of deep relu networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning is robust to massive label noise"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a theoretical framework for deep and locally connected relu network"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (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"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "elf opengo an analysis and open reimplementation of alphazero"):
  with members:
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "from gameplay to symbolic reasoning learning sat solver heuristics in the style of alpha go zero"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "demystifying alphago zero as alphago gan"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "move evaluation in go using deep convolutional neural networks"
    - "teaching deep convolutional neural networks to play go"

Connected through hyperedge HE-5 (induced by source paper "playing go without game tree search using convolutional neural networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep residual learning for image recognition"
    - "move evaluation in go using deep convolutional neural networks"
    - "going deeper with convolutions"
    - "teaching deep convolutional neural networks to play go"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "multiple policy value monte carlo tree search"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "deep residual learning for image recognition"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"
    - "teaching deep convolutional neural networks to play go"

Connected through hyperedge HE-7 (induced by source paper "pfml based semantic bci agent for game of go learning and prediction"):
  with members:
    - "pso based fuzzy markup language for student learning performance evaluation and educational application"
    - "human and smart machine co learning brain computer interaction at the 2017 ieee international conference on systems man and cybernetics"
    - "ontology based fuzzy markup language agent for student and robot co learning"
    - "fml based dynamic assessment agent for human machine cooperative system on game of go"

Connected through hyperedge HE-8 (induced by source paper "a gfml based robot agent for human and machine cooperative learning on game of go"):
  with members:
    - "pso based fuzzy markup language for student learning performance evaluation and educational application"
    - "fml based dynamic assessment agent for human machine cooperative system on game of 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)