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: "move evaluation in go using deep convolutional neural networks"
  Abstract: The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move.

Connected through hyperedge HE-1 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "dota 2 with large scale deep reinforcement learning"
    - "on the power of curriculum learning in training deep networks"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation"
    - "zpd teaching strategies for deep reinforcement learning from demonstrations"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"

Connected through hyperedge HE-3 (induced by source paper "generator evaluator selector net a modular approach for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "learning instance occlusion for panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "a review of modularization techniques in artificial neural networks"
    - "upsnet a unified panoptic segmentation network"
    - "selectivenet a deep neural network with an integrated reject option"
    - "class independent sequential full image segmentation using a convolutional net that finds a segment within an attention region given a pointer pixel within this segment"
    - "panoptic feature pyramid networks"

Connected through hyperedge HE-4 (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-5 (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"
    - "better computer go player with neural network and long term prediction"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "teaching deep convolutional neural networks to play go"

Connected through hyperedge HE-6 (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"
    - "better computer go player with neural network and long term prediction"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "teaching deep convolutional neural networks to play go"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "planning chemical syntheses with deep neural networks and symbolic ai"):
  with members:
    - "end to end differentiable proving"
    - "modelling chemical reasoning to predict and invent reactions"
    - "monte carlo tableau proof search"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "building machines that learn and think like people"
    - "theano a python framework for fast computation of mathematical expressions"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "convolutional invasion and expansion networks for tumor growth prediction"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "deeppap deep convolutional networks for cervical cell classification"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "unsupervised learning for physical interaction through video prediction"
    - "long term image boundary extrapolation"
    - "convolutional two stream network fusion for video action recognition"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "automatic segmentation of mr brain images with a convolutional neural network"

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)