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: "teaching deep convolutional neural networks to play go"
  Abstract: Mastering the game of Go has remained a long standing challenge to the field of AI. Modern computer Go systems rely on processing millions of possible future positions to play well, but intuitively a stronger and more 'humanlike' way to play the game would be to rely on pattern recognition abilities rather then brute force computation. Following this sentiment, we train deep convolutional neural networks to play Go by training them to predict the moves made by expert Go players. To solve this problem we introduce a number of novel techniques, including a method of tying weights in the network to 'hard code' symmetries that are expect to exist in the target function, and demonstrate in an ablation study they considerably improve performance. Our final networks are able to achieve move prediction accuracies of 41.1% and 44.4% on two different Go datasets, surpassing previous state of the art on this task by significant margins. Additionally, while previous move prediction programs have not yielded strong Go playing programs, we show that the networks trained in this work acquired high levels of skill. Our convolutional neural networks can consistently defeat the well known Go program GNU Go, indicating it is state of the art among programs that do not use Monte Carlo Tree Search. It is also able to win some games against state of the art Go playing program Fuego while using a fraction of the play time. This success at playing Go indicates high level principles of the game were learned.

Connected through hyperedge HE-1 (induced by source paper "analysis of hyper parameters for small games iterations or epochs in self play"):
  with members:
    - "monte carlo q learning for general game playing"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "combining simulated annealing and monte carlo tree search for expression simplification"

Connected through hyperedge HE-2 (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-3 (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"
    - "move evaluation in go using deep convolutional neural networks"

Connected through hyperedge HE-4 (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"
    - "move evaluation in go using deep convolutional neural networks"
    - "going deeper with convolutions"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "hyper parameter sweep on alphazero general"):
  with members:
    - "monte carlo q learning for general game playing"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "combining simulated annealing and monte carlo tree search for expression simplification"

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"
    - "better computer go player with neural network and long term prediction"
    - "deep residual learning for image recognition"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"

Connected through hyperedge HE-7 (induced by source paper "non parametric transformation networks"):
  with members:
    - "max margin invariant features from transformed unlabeled data"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "graph based isometry invariant representation learning"
    - "harmonic networks deep translation and rotation equivariance"
    - "learning to extract motion from videos in convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "harmonic adversarial attack method"):
  with members:
    - "adversarial patch"
    - "robust physical world attacks on deep learning models"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "synthesizing robust adversarial examples"
    - "generating natural adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "densely connected convolutional networks"
    - "towards evaluating the robustness of neural 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)