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: "compressing neural language models by sparse word representations"
  Abstract: Neural networks are among the state-of-the-art techniques for language modeling. Existing neural language models typically map discrete words to distributed, dense vector representations. After information processing of the preceding context words by hidden layers, an output layer estimates the probability of the next word. Such approaches are time- and memory-intensive because of the large numbers of parameters for word embeddings and the output layer. In this paper, we propose to compress neural language models by sparse word representations. In the experiments, the number of parameters in our model increases very slowly with the growth of the vocabulary size, which is almost imperceptible. Moreover, our approach not only reduces the parameter space to a large extent, but also improves the performance in terms of the perplexity measure.

Connected through hyperedge HE-1 (induced by source paper "define deep factorized input word embeddings for neural sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-2 (induced by source paper "sparsity emerges naturally in neural language models"):
  with members:
    - "a deep generative model of vowel formant typology"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "understanding learning dynamics of language models with svcca"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "exploring sparsity in recurrent neural networks"
    - "opening the black box of deep neural networks via information"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-3 (induced by source paper "slim embedding layers for recurrent neural language models"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "efficient softmax approximation for gpus"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"
    - "recurrent highway networks"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "using the output embedding to improve language models"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-4 (induced by source paper "sparse coding of neural word embeddings for multilingual sequence labeling"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "unsupervised pos induction with word embeddings"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "sparse overcomplete word vector representations"
    - "learning word representations with hierarchical sparse coding"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)