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: "document similarity for texts of varying lengths via hidden topics"
  Abstract: Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of incorporating domain knowledge to text matching.

Connected through hyperedge HE-1 (induced by source paper "wasserstein fisher rao document distance"):
  with members:
    - "word mover s embedding from word2vec to document embedding"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "advances in pre training distributed word representations"
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "semantic matching of documents from heterogeneous collections a simple and transparent method for practical applications"):
  with members:
    - "transparent efficient and robust word embedding access with wombat"
    - "deep contextualized word representations"
    - "enriching word vectors with subword information"
    - "a compare aggregate model for matching text sequences"
    - "applying deep learning to answer selection a study and an open task"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "pare a paper reviewer matching approach using a common topic space"):
  with members:
    - "weakly learning to match experts in online community"
    - "geometry of compositionality"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "what the f measure doesn t measure features flaws fallacies and fixes"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "word mover s embedding from word2vec to document embedding"):
  with members:
    - "embedding syntax and semantics of prepositions via tensor decomposition"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "sentence state lstm for text representation"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "prepositions in context"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "a latent variable model approach to pmi based word embeddings"

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)