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: "measuring word significance using distributed representations of words"
  Abstract: Distributed representations of words as real-valued vectors in a relatively low-dimensional space aim at extracting syntactic and semantic features from large text corpora. A recently introduced neural network, named word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic information in the direction of the word vectors. In this brief report, it is proposed to use the length of the vectors, together with the term frequency, as measure of word significance in a corpus. Experimental evidence using a domain-specific corpus of abstracts is presented to support this proposal. A useful visualization technique for text corpora emerges, where words are mapped onto a two-dimensional plane and automatically ranked by significance.

Connected through hyperedge HE-1 (induced by source paper "don t settle for average go for the max fuzzy sets and max pooled word vectors"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "context aware zero shot learning for object recognition"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "modular generative adversarial networks"
    - "deep contextualized word representations"
    - "context encoding for semantic segmentation"
    - "semantic autoencoder for zero shot learning"
    - "learning transferable architectures for scalable image recognition"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "analyzing structures in the semantic vector space a framework for decomposing word embeddings"):
  with members:
    - "imparting interpretability to word embeddings while preserving semantic structure"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "recent trends in deep learning based natural language processing"
    - "intrinsic subspace evaluation of word embedding representations"
    - "linear algebraic structure of word senses with applications to polysemy"

Connected through hyperedge HE-4 (induced by source paper "model comparison for semantic grouping"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "towards universal paraphrastic sentence embeddings"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "context matters recovering human semantic structure from machine learning analysis of large scale text corpora"):
  with members:
    - "semantic projection recovering human knowledge of multiple distinct object features from word embeddings"
    - "semantics derived automatically from language corpora contain human like biases"
    - "semantic compositional networks for visual captioning"
    - "enriching word vectors with subword information"
    - "deep compositional captioning describing novel object categories without paired training data"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "what the vec towards probabilistically grounded embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "on understanding knowledge graph representation"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "quantum inspired complex word embedding"
    - "towards understanding linear word analogies"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "towards understanding linear word analogies"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "on the dimensionality of word embedding"
    - "a latent variable model approach to pmi based word embeddings"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "controlled experiments for word embeddings"):
  with members:
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)