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: "matrix factorization using window sampling and negative sampling for improved word representations"
  Abstract: In this paper, we propose LexVec, a new method for generating distributed word representations that uses low-rank, weighted factorization of the Positive Point-wise Mutual Information matrix via stochastic gradient descent, employing a weighting scheme that assigns heavier penalties for errors on frequent co-occurrences while still accounting for negative co-occurrence. Evaluation on word similarity and analogy tasks shows that LexVec matches and often outperforms state-of-the-art methods on many of these tasks.

Connected through hyperedge HE-1 (induced by source paper "a common semantic space for monolingual and cross lingual meta embeddings"):
  with members:
    - "sequence tagging with contextual and non contextual subword representations a multilingual evaluation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "bilingual embeddings with random walks over multilingual wordnets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "think globally embed locally locally linear meta embedding of words"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "supervised phrase boundary embeddings"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "enriching word vectors with subword information"
    - "adaptive joint learning of compositional and non compositional phrase embeddings"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"
    - "on using very large target vocabulary for neural machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "affinity a system for latent user similarity comparison on texting data"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "context data categories and privacy model for mobile data collection apps"
    - "do you like what i like similarity estimation in proximity based mobile social networks"
    - "towards psychometrics based friend recommendations in social networking services"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "creating a live public short message service corpus the nus sms corpus"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-4 (induced by source paper "why so down the role of negative and positive pointwise mutual information in distributional semantics"):
  with members:
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "swivel improving embeddings by noticing what s missing"
    - "bidirectional lstm crf models for sequence tagging"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "measuring praise and criticism inference of semantic orientation from association"

Connected through hyperedge HE-5 (induced by source paper "parameter free sentence embedding via orthogonal basis"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-6 (induced by source paper "semi supervised multi task word embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-7 (induced by source paper "meta embedding as auxiliary task regularization"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"
    - "learning word meta embeddings by using ensembles of embedding sets"

Connected through hyperedge HE-8 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "evaluation of croatian word embeddings"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "issues in evaluating semantic spaces using word analogies"
    - "unsupervised dialogue act induction using gaussian mixtures"

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)