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: "incorporating glosses into neural word sense disambiguation"
  Abstract: Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-theart systems on several English all-words WSD datasets.

Connected through hyperedge HE-1 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence"):
  with members:
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "implementing a reverse dictionary based on word definitions using a node graph architecture"
    - "neural machine translation of rare words with subword units"
    - "learning to understand phrases by embedding the dictionary"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "language modelling makes sense propagating representations through wordnet for full coverage word sense disambiguation"):
  with members:
    - "liaad at semdeep 5 challenge word in context wic"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "improving the coverage and the generalization ability of neural word sense disambiguation through hypernymy and hyponymy relationships"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "semi supervised word sense disambiguation with neural models"

Connected through hyperedge HE-3 (induced by source paper "improved word sense disambiguation using pre trained contextualized word representations"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "language modeling with gated convolutional networks"
    - "word sense disambiguation using a bidirectional lstm"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "multi channel reverse dictionary model"):
  with members:
    - "enhancing recurrent neural networks with sememes"
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "openhownet an open sememe based lexical knowledge base"
    - "using multi sense vector embeddings for reverse dictionaries"
    - "building a knowledge graph from natural language definitions for interpretable text entailment recognition"
    - "learning to compute word embeddings on the fly"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-5 (induced by source paper "pun gan generative adversarial network for pun generation"):
  with members:
    - "pun generation with surprise"
    - "long text generation via adversarial training with leaked information"
    - "a deep reinforced model for abstractive summarization"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "improved techniques for training gans"
    - "word sense disambiguation using a bidirectional lstm"
    - "word sense disambiguation a survey"

Connected through hyperedge HE-6 (induced by source paper "using bert for word sense disambiguation"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "sense vocabulary compression through the semantic knowledge of wordnet for neural word sense disambiguation"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "incremental sense weight training for the interpretation of contextualized word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "spine sparse interpretable neural embeddings"
    - "linear algebraic structure of word senses with applications to polysemy"

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)