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: "embedding words and senses together via joint knowledge enhanced training"
  Abstract: Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector. We address this issue by proposing a new model which learns word and sense embeddings jointly. Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings. We evaluate the main features of our approach both qualitatively and quantitatively in a variety of tasks, highlighting the advantages of the proposed method in comparison to state-of-the-art word- and sense-based models.

Connected through hyperedge HE-1 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "knowledge based word sense disambiguation using topic models"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "distributional measures of semantic distance a survey"

Connected through hyperedge HE-2 (induced by source paper "modelling semantic categories using conceptual neighborhood"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"
    - "learning conceptual space representations of interrelated concepts"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "nonparametric spherical topic modeling with word embeddings"
    - "entity embeddings with conceptual subspaces as a basis for plausible reasoning"

Connected through hyperedge HE-3 (induced by source paper "multi sense embeddings from topic models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "making sense of word embeddings"
    - "a mixture model for learning multi sense word embeddings"
    - "de conflated semantic representations"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "do multi sense embeddings improve natural language understanding"

Connected through hyperedge HE-4 (induced by source paper "one homonym per translation"):
  with members:
    - "handling homographs in neural machine translation"
    - "towards a seamless integration of word senses into downstream nlp applications"
    - "one sense per collocation and genre topic variations"

Connected through hyperedge HE-5 (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-6 (induced by source paper "seven augmenting word embeddings with unsupervised relation vectors"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "modeling semantic relatedness using global relation vectors"
    - "specialising word vectors for lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "classifying relations via long short term memory networks along shortest dependency path"

Connected through hyperedge HE-7 (induced by source paper "representation learning of entities and documents from knowledge base descriptions"):
  with members:
    - "corpus level fine grained entity typing using contextual information"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "skip thought vectors"
    - "towards universal paraphrastic sentence embeddings"
    - "learning to understand phrases by embedding the dictionary"
    - "inferring missing entity type instances for knowledge base completion new dataset and methods"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "a short survey on sense annotated corpora"):
  with members:
    - "language modelling makes sense propagating representations through wordnet for full coverage word sense disambiguation"
    - "glossbert bert for word sense disambiguation with gloss knowledge"
    - "incorporating glosses into neural word sense disambiguation"
    - "huge automatically extracted training sets for multilingual word sense disambiguation"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "named entity disambiguation for noisy text"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"

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)