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: "time out temporal referencing for robust modeling of lexical semantic change"
  Abstract: State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.

Connected through hyperedge HE-1 (induced by source paper "capturing evolution in word usage just add more clusters"):
  with members:
    - "towards better substitution based word sense induction"
    - "does bert make any sense interpretable word sense disambiguation with contextualized embeddings"
    - "simple dynamic word embeddings for mapping perceptions in the public sphere"
    - "leveraging contextual embeddings for detecting diachronic semantic shift"
    - "survey of computational approaches to diachronic conceptual change"
    - "a state of the art of semantic change computation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "shared task lexical semantic change detection in german"):
  with members:
    - "a wind of change detecting and evaluating lexical semantic change across times and domains"
    - "survey of computational approaches to diachronic conceptual change"
    - "fasttext zip compressing text classification models"
    - "word translation without parallel data"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "sparse overcomplete word vector representations"
    - "statistically significant detection of linguistic change"
    - "temporal analysis of language through neural language models"

Connected through hyperedge HE-3 (induced by source paper "gasc genre aware semantic change for ancient greek"):
  with members:
    - "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"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "temporal analysis of language through neural language 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)