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: "enhancing the lexvec distributed word representation model using positional contexts and external memory"
  Abstract: In this paper we take a state-of-the-art model for distributed word representation that explicitly factorizes the positive pointwise mutual information (PPMI) matrix using window sampling and negative sampling and address two of its shortcomings. We improve syntactic performance by using positional contexts, and solve the need to store the PPMI matrix in memory by working on aggregate data in external memory. The effectiveness of both modifications is shown using word similarity and analogy tasks.

Connected through hyperedge HE-1 (induced by source paper "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain"):
  with members:
    - "redefining context windows for word embedding models an experimental study"
    - "high risk learning acquiring new word vectors from tiny data"
    - "the effects of data size and frequency range on distributional semantic models"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "issues in evaluating semantic spaces using word analogies"
    - "mining and modeling character networks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (induced by source paper "relation extraction datasets in the digital humanities domain and their evaluation with word embeddings"):
  with members:
    - "negative sampling improves hypernymy extraction based on projection learning"
    - "enriching word vectors with subword information"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "mining and modeling character networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "using word embeddings for visual data exploration with ontodia and wikidata"):
  with members:
    - "question answering on linked data challenges and future directions"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "incorporating subword information into matrix factorization word embeddings"):
  with members:
    - "character word lstm language models"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "morphological priors for probabilistic neural word embeddings"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-5 (induced by source paper "matching natural language sentences with hierarchical sentence factorization"):
  with members:
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "bilateral multi perspective matching for natural language sentences"
    - "an incremental parser for abstract meaning representation"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "bag of tricks for efficient text classification"
    - "text matching as image recognition"
    - "a compare aggregate model for matching text sequences"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-6 (induced by source paper "object and text guided semantics for cnn based activity recognition"):
  with members:
    - "incorporating subword information into matrix factorization word embeddings"
    - "iod cnn integrating object detection networks for event recognition"
    - "exploitation of semantic keywords for malicious event classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "bag of tricks for efficient text classification"
    - "an all in one convolutional neural network for face analysis"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "neobility at semeval 2017 task 1 an attention based sentence similarity model"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "learning distributed representations of sentences from unlabelled data"
    - "charagram embedding words and sentences via character n grams"

Connected through hyperedge HE-8 (induced by source paper "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"):
  with members:
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"

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)