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: "deepchess end to end deep neural network for automatic learning in chess"
  Abstract: We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated.

Connected through hyperedge HE-1 (induced by source paper "deepmimic mentor student unlabeled data based training"):
  with members:
    - "stealing knowledge from protected deep neural networks using composite unlabeled data"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "robust student network learning"
    - "learning transferable architectures for scalable image recognition"
    - "low shot learning with imprinted weights"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "automated chess commentator powered by neural chess engine"):
  with members:
    - "press space to fire automatic video game tutorial generation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "generating levels that teach mechanics"
    - "order planning neural text generation from structured data"
    - "challenges in data to document generation"
    - "story generation from sequence of independent short descriptions"
    - "effective approaches to attention based neural machine translation"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-3 (induced by source paper "sentimate learning to play chess through natural language processing"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "giraffe using deep reinforcement learning to play chess"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "feature based aggregation and deep reinforcement learning a survey and some new implementations"):
  with members:
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "deep reinforcement learning a brief survey"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep learning in neural networks"
    - "discretized approximations for pomdp with average cost"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-5 (induced by source paper "vain attentional multi agent predictive modeling"):
  with members:
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "gated graph sequence neural networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "learning multiagent communication with backpropagation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "mastering chess and shogi by self play with a general reinforcement learning algorithm"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "thinking fast and slow with deep learning and tree search"
    - "giraffe using deep reinforcement learning to play chess"
    - "move evaluation in go using deep convolutional 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.NE(Neural and Evolutionary Computing)