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: "freshman or fresher quantifying the geographic variation of internet language"
  Abstract: We present a new computational technique to detect and analyze statistically significant geographic variation in language. Our meta-analysis approach captures statistical properties of word usage across geographical regions and uses statistical methods to identify significant changes specific to regions. While previous approaches have primarily focused on lexical variation between regions, our method identifies words that demonstrate semantic and syntactic variation as well. #R##N#We extend recently developed techniques for neural language models to learn word representations which capture differing semantics across geographical regions. In order to quantify this variation and ensure robust detection of true regional differences, we formulate a null model to determine whether observed changes are statistically significant. Our method is the first such approach to explicitly account for random variation due to chance while detecting regional variation in word meaning. #R##N#To validate our model, we study and analyze two different massive online data sets: millions of tweets from Twitter spanning not only four different countries but also fifty states, as well as millions of phrases contained in the Google Book Ngrams. Our analysis reveals interesting facets of language change at multiple scales of geographic resolution -- from neighboring states to distant continents. #R##N#Finally, using our model, we propose a measure of semantic distance between languages. Our analysis of British and American English over a period of 100 years reveals that semantic variation between these dialects is shrinking.

Connected through hyperedge HE-1 (induced by source paper "dialectgram detecting dialectal variation at multiple geographic resolutions"):
  with members:
    - "using sentiment induction to understand variation in gendered online communities"
    - "dialectometric analysis of language variation in twitter"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "statistically significant detection of linguistic change"
    - "temporal analysis of language through neural language models"
    - "diffusion of lexical change in social media"

Connected through hyperedge HE-2 (induced by source paper "on the linearity of semantic change investigating meaning variation via dynamic graph models"):
  with members:
    - "opinion dynamics and wisdom under out group discrimination"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation for named entity recognition in online media with word embeddings"):
  with members:
    - "online updating of word representations for part of speech tagging"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "bidirectional lstm crf models for sequence tagging"
    - "unsupervised domain adaptation with feature embeddings"
    - "temporal analysis of language through neural language models"
    - "gender identity and lexical variation in social media"
    - "the expressive power of word embeddings"

Connected through hyperedge HE-4 (induced by source paper "language classification from bilingual word embedding graphs"):
  with members:
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "comparing fifty natural languages and twelve genetic languages using word embedding language divergence weld as a quantitative measure of language distance"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "multilingual models for compositional distributed semantics"
    - "an autoencoder approach to learning bilingual word representations"

Connected through hyperedge HE-5 (induced by source paper "visualizing linguistic shift"):
  with members:
    - "on minimizing crossings in storyline visualizations"
    - "efficient estimation of word representations in vector space"
    - "distributional measures of semantic distance a survey"

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)