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: "do neural nets learn statistical laws behind natural language"
  Abstract: The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf’s law and Heaps’ law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf’s law and Heaps’ law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.

Connected through hyperedge HE-1 (induced by source paper "mutual information scaling and expressive power of sequence models"):
  with members:
    - "sharp nearby fuzzy far away how neural language models use context"
    - "estimating information flow in neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "opening the black box of deep neural networks via information"
    - "sgdr stochastic gradient descent with restarts"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-2 (induced by source paper "evaluating computational language models with scaling properties of natural language"):
  with members:
    - "taylor s law for human linguistic sequences"
    - "an analysis of neural language modeling at multiple scales"
    - "cot cooperative training for generative modeling of discrete data"
    - "texygen a benchmarking platform for text generation models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "adversarial generation of natural language"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "natural language statistical features of lstm generated texts"):
  with members:
    - "lstm a search space odyssey"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "visualizing and understanding recurrent networks"
    - "sequence to sequence video to text"
    - "recurrent neural network regularization"
    - "deep visual semantic alignments for generating image descriptions"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "assessing language models with scaling properties"):
  with members:
    - "taylor s law for human linguistic sequences"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "improving neural language models with a continuous cache"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"

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.CL(Computation and Language)