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: "that s sick dude automatic identification of word sense change across different timescales"
  Abstract: In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distributional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we compare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation indicates that the algorithm could correctly identify 60.4% birth cases from a set of 48 randomly picked samples and 57% split/join cases from a set of 21 randomly picked samples. Remarkably, in 44% cases the birth of a novel sense is attested by WordNet, while in 46% cases and 43% cases split and join are respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.

Connected through hyperedge HE-1 (induced by source paper "time out temporal referencing for robust modeling of lexical semantic change"):
  with members:
    - "gasc genre aware semantic change for ancient greek"
    - "survey of computational approaches to lexical semantic change"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "german in flux detecting metaphoric change via word entropy"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "diachronic word embeddings reveal statistical laws of semantic change"

Connected through hyperedge HE-2 (induced by source paper "back to the future sequential alignment of text representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a review of domain adaptation without target labels"
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rumoureval 2019 determining rumour veracity and support for rumours"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"

Connected through hyperedge HE-3 (induced by source paper "gasc genre aware semantic change for ancient greek"):
  with members:
    - "time out temporal referencing for robust modeling of lexical semantic change"
    - "survey of computational approaches to diachronic conceptual change"
    - "a state of the art of semantic change computation"
    - "survey of computational approaches to lexical semantic change"
    - "modeling language change in historical corpora the case of portuguese"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"
    - "temporal analysis of language through neural language models"

Connected through hyperedge HE-4 (induced by source paper "generating timelines by modeling semantic change"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "vector embedding of wikipedia concepts and entities"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "timemachine timeline generation for knowledge base entities"
    - "statistically significant detection of linguistic change"
    - "timeline generation tracking individuals on twitter"

Connected through hyperedge HE-5 (induced by source paper "survey of computational approaches to lexical semantic change"):
  with members:
    - "the road to success assessing the fate of linguistic innovations in online communities"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "modeling the dynamics of domain specific terminology in diachronic corpora"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "detecting and assessing contextual change in diachronic text documents using context volatility"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"

Connected through hyperedge HE-6 (induced by source paper "survey of computational approaches to diachronic conceptual change"):
  with members:
    - "survey of computational approaches to lexical semantic change"
    - "the road to success assessing the fate of linguistic innovations in online communities"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "detecting and assessing contextual change in diachronic text documents using context volatility"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "the social dynamics of language change in online networks"

Connected through hyperedge HE-7 (induced by source paper "diachronic word embeddings and semantic shifts a survey"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "incremental skip gram model with negative sampling"
    - "learning word relatedness over time"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-8 (induced by source paper "the global anchor method for quantifying linguistic shifts and domain adaptation"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "on the dimensionality of word embedding"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "a latent variable model approach to pmi based word embeddings"
    - "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)