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: "towards a seamless integration of word senses into downstream nlp applications"
  Abstract: Lexical ambiguity can impede NLP systems from accurate understanding of semantics. Despite its potential benefits, the integration of sense-level information into NLP systems has remained understudied. By incorporating a novel disambiguation algorithm into a state-of-the-art classification model, we create a pipeline to integrate sense-level information into downstream NLP applications. We show that a simple disambiguation of the input text can lead to consistent performance improvement on multiple topic categorization and polarity detection datasets, particularly when the fine granularity of the underlying sense inventory is reduced and the document is sufficiently large. Our results also point to the need for sense representation research to focus more on in vivo evaluations which target the performance in downstream NLP applications rather than artificial benchmarks.

Connected through hyperedge HE-1 (induced by source paper "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-2 (induced by source paper "neural attentive bag of entities model for text classification"):
  with members:
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "studio ousia s quiz bowl question answering system"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"

Connected through hyperedge HE-3 (induced by source paper "using multi sense vector embeddings for reverse dictionaries"):
  with members:
    - "one single deep bidirectional lstm network for word sense disambiguation of text data"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "learning sentence embeddings using recursive networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-4 (induced by source paper "linspector web a multilingual probing suite for word representations"):
  with members:
    - "linspector multilingual probing tasks for word representations"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "analysis methods in neural language processing a survey"
    - "understanding learning dynamics of language models with svcca"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "wic the word in context dataset for evaluating context sensitive meaning representations"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "de conflated semantic representations"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "one homonym per translation"):
  with members:
    - "handling homographs in neural machine translation"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "one sense per collocation and genre topic variations"

Connected through hyperedge HE-7 (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-8 (induced by source paper "dynamic integration of background knowledge in neural nlu systems"):
  with members:
    - "natural language inference over interaction space"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"
    - "making neural qa as simple as possible but not simpler"
    - "a neural language model for dynamically representing the meanings of unknown words and entities in a discourse"
    - "task oriented query reformulation with reinforcement learning"
    - "a broad coverage challenge corpus for sentence understanding through inference"

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)