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: "semi supervised word sense disambiguation with neural models"
  Abstract: Determining the intended sense of words in text - word sense disambiguation (WSD) - is a long standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.

Connected through hyperedge HE-1 (induced by source paper "does bert make any sense interpretable word sense disambiguation with contextualized embeddings"):
  with members:
    - "sense vocabulary compression through the semantic knowledge of wordnet for neural word sense disambiguation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "roberta a robustly optimized bert pretraining approach"
    - "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"

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"
    - "incorporating glosses into neural word sense disambiguation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "using bert for word sense disambiguation"):
  with members:
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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-4 (induced by source paper "sense vocabulary compression through the semantic knowledge of wordnet for neural word sense disambiguation"):
  with members:
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "word sense disambiguation using a bidirectional lstm"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "towards better substitution based word sense induction"):
  with members:
    - "autosense model for word sense induction"
    - "deep contextualized word representations"
    - "word sense induction with neural bilm and symmetric patterns"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-6 (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-7 (induced by source paper "a novel neural sequence model with multiple attentions for word sense disambiguation"):
  with members:
    - "one single deep bidirectional lstm network for word sense disambiguation of text data"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "a game theoretic approach to word sense disambiguation"
    - "word sense disambiguation using a bidirectional lstm"
    - "linguistic input features improve neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "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)