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: "unsupervised knowledge free and interpretable word sense disambiguation"
  Abstract: Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.

Connected through hyperedge HE-1 (induced by source paper "word sense disambiguation for 158 languages using word embeddings only"):
  with members:
    - "glossbert bert for word sense disambiguation with gloss knowledge"
    - "multi sense embeddings from topic models"
    - "watset local global graph clustering with applications in sense and frame induction"
    - "an unsupervised word sense disambiguation system for under resourced languages"
    - "joint multilingual supervision for cross lingual entity linking"
    - "russian word sense induction by clustering averaged word embeddings"
    - "autosense model for word sense induction"
    - "word sense induction with neural bilm and symmetric patterns"

Connected through hyperedge HE-2 (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-3 (induced by source paper "russian word sense induction by clustering averaged word embeddings"):
  with members:
    - "word sense induction using word embeddings and community detection in complex networks"
    - "size vs structure in training corpora for word embedding models araneum russicum maximum and russian national corpus"
    - "clustering comparable corpora of russian and ukrainian academic texts word embeddings and semantic fingerprints"
    - "enriching word vectors with subword information"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-4 (induced by source paper "an unsupervised word sense disambiguation system for under resourced languages"):
  with members:
    - "watset automatic induction of synsets from a graph of synonyms"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "how much does a word weigh weighting word embeddings for word sense induction"):
  with members:
    - "word sense induction using word embeddings and community detection in complex networks"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "a framework for enriching lexical semantic resources with distributional semantics"):
  with members:
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "multilingual open relation extraction using cross lingual projection"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "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"
    - "retrofitting word vectors to semantic lexicons"
    - "distributed representations of words and phrases and their compositionality"

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)