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: "from word to sense embeddings a survey on vector representations of meaning"
  Abstract: Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.

Connected through hyperedge HE-1 (induced by source paper "tedl a text encryption method based on deep learning"):
  with members:
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "lightweight efficient multi keyword ranked search over encrypted cloud data using dual word embeddings"
    - "recent trends in deep learning based natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "membership inference attacks against machine learning models"
    - "towards biologically plausible deep learning"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "deep contextualized word representations"
    - "knowledge based word sense disambiguation using topic models"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "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-3 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep learning for sentiment analysis a survey"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "conversational networks for automatic online moderation"
    - "remembering winter was coming"
    - "predicting kills in game of thrones using network properties"
    - "multilayer network model of movie script"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"
    - "a survey of comics research in computer science"

Connected through hyperedge HE-5 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conditional generators of words definitions"
    - "factors influencing the surprising instability of word embeddings"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "verb argument structure alternations in word and sentence embeddings"

Connected through hyperedge HE-6 (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"
    - "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"
    - "ontology aware token embeddings for prepositional phrase attachment"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "learning taxonomies of concepts and not words using contextualized word representations a position paper"):
  with members:
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "taxonomy induction using hypernym subsequences"
    - "distributional inclusion vector embedding for unsupervised hypernymy detection"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "commonly uncommon semantic sparsity in situation recognition"

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)