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: "joint word representation learning using a corpus and a semantic lexicon"
  Abstract: Methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representation learning methods do not consider the rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNet that represent the meanings of words by defining the various relationships that exist among the words in a language. We consider the question, can we improve the word representations learnt using a corpora by integrating the knowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon. We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus. Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into word representations on several benchmark datasets for semantic similarity and word analogy.

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 "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-3 (induced by source paper "imparting interpretability to word embeddings while preserving semantic structure"):
  with members:
    - "analytical methods for interpretable ultradense word embeddings"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "spine sparse interpretable neural embeddings"
    - "jointly learning word embeddings and latent topics"

Connected through hyperedge HE-4 (induced by source paper "aff2vec affect enriched distributional word representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "wassa 2017 shared task on emotion intensity"
    - "affect lm a neural language model for customizable affective text generation"
    - "a neural knowledge language model"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "from paraphrase database to compositional paraphrase model and back"

Connected through hyperedge HE-5 (induced by source paper "bilingual embeddings with random walks over multilingual wordnets"):
  with members:
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "bilingual distributed word representations from document aligned comparable data"
    - "a large annotated corpus for learning natural language inference"
    - "deepwalk online learning of social representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"
    - "multilingual models for compositional distributed semantics"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "can ai generate love advice toward neural answer generation for non factoid questions"):
  with members:
    - "applying deep learning to answer selection a study and an open task"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a neural conversational model"
    - "lstm based deep learning models for non factoid answer selection"
    - "deep learning for answer sentence selection"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"

Connected through hyperedge HE-7 (induced by source paper "learning domain specific word embeddings from sparse cybersecurity texts"):
  with members:
    - "characterizing diseases from unstructured text a vocabulary driven word2vec approach"
    - "neural architectures for named entity recognition"
    - "boosting named entity recognition with neural character embeddings"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "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 "improve lexicon based word embeddings by word sense disambiguation"):
  with members:
    - "bag of tricks for efficient text classification"
    - "distributed representations of sentences and documents"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "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)