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: "word sense induction with neural bilm and symmetric patterns"
  Abstract: An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors. #R##N#We replace the ngram-based language model (LM) with a recurrent one. Beyond being more accurate, the use of the recurrent LM allows us to effectively query it in a creative way, using what we call dynamic symmetric patterns. #R##N#The combination of the RNN-LM and the dynamic symmetric patterns results in strong substitute vectors for WSI, allowing to surpass the current state-of-the-art on the SemEval 2013 WSI shared task by a large margin.

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"
    - "learning word vectors for 157 languages"

Connected through hyperedge HE-2 (induced by source paper "still a pain in the neck evaluating text representations on lexical composition"):
  with members:
    - "semeval 2013 task 4 free paraphrases of noun compounds"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "allennlp a deep semantic natural language processing platform"
    - "olive oil is made of olives baby oil is made for babies interpreting noun compounds using paraphrases in a neural model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "inducing relational knowledge from bert"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "relational word embeddings"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "do neural language representations learn physical commonsense"

Connected through hyperedge HE-4 (induced by source paper "towards better substitution based word sense induction"):
  with members:
    - "autosense model for word sense induction"
    - "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"

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)