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: "evaluating neural word representations in tensor based compositional settings"
  Abstract: We provide a comparative study between neural word representations and traditional vector spaces based on co-occurrence counts, in a number of compositional tasks. We use three different semantic spaces and implement seven tensor-based compositional models, which we then test (together with simpler additive and multiplicative approaches) in tasks involving verb disambiguation and sentence similarity. To check their scalability, we additionally evaluate the spaces using simple compositional methods on larger-scale tasks with less constrained language: paraphrase detection and dialogue act tagging. In the more constrained tasks, co-occurrence vectors are competitive, although choice of compositional method is important; on the larger-scale tasks, they are outperformed by neural word embeddings, which show robust, stable performance across the tasks.

Connected through hyperedge HE-1 (induced by source paper "deep image to video adaptation and fusion networks for action recognition"):
  with members:
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "combined static and motion features for deep networks based activity recognition in videos"
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "global temporal representation based cnns for infrared action recognition"
    - "deep cross media knowledge transfer"
    - "exploiting images for video recognition with hierarchical generative adversarial networks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "efficient sentence embedding using discrete cosine transform"):
  with members:
    - "evaluation of unsupervised compositional representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "advances in pre training distributed word representations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-3 (induced by source paper "a typedriven vector semantics for ellipsis with anaphora using lambek calculus with limited contraction"):
  with members:
    - "classical copying versus quantum entanglement in natural language the case of vp ellipsis"
    - "a generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras"
    - "quantifier scope in categorical compositional distributional semantics"
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "coordination in categorical compositional distributional semantics"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "distributed representations of words and phrases and their compositionality"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-4 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "alternative semantic representations for zero shot human action recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "evaluation of unsupervised compositional representations"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-6 (induced by source paper "sentence entailment in compositional distributional semantics"):
  with members:
    - "a generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras"
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "coordination in categorical compositional distributional semantics"
    - "graded entailment for compositional distributional semantics"
    - "a frobenius model of information structure in categorical compositional distributional semantics"
    - "open system categorical quantum semantics in natural language processing"
    - "distributional sentence entailment using density matrices"
    - "compositional distributional semantics with compact closed categories and frobenius algebras"

Connected through hyperedge HE-7 (induced by source paper "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"):
  with members:
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "node2vec scalable feature learning for networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "learning distributed representations from reviews for collaborative filtering"
    - "towards universal paraphrastic sentence embeddings"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "unsupervised cross lingual information retrieval using monolingual data only"):
  with members:
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "cross language question re ranking"
    - "neural models for information retrieval"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "bilingual distributed word representations from document aligned comparable data"
    - "multilingual models for compositional distributed semantics"
    - "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)