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: "characterizing the language of online communities and its relation to community reception"
  Abstract: This work investigates style and topic aspects of language in online communities: looking at both utility as an identifier of the community and correlation with community reception of content. Style is characterized using a hybrid word and part-of-speech tag n-gram language model, while topic is represented using Latent Dirichlet Allocation. Experiments with several Reddit forums show that style is a better indicator of community identity than topic, even for communities organized around specific topics. Further, there is a positive correlation between the community reception to a contribution and the style similarity to that community, but not so for topic similarity.

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "using word embeddings in twitter election classification"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-2 (induced by source paper "models for predicting community specific interest in news articles"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "the impact of crowds on news engagement a reddit case study"
    - "breaking the news first impressions matter on online news"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "talking to the crowd what do people react to in online discussions"
    - "a bayesian approach for predicting the popularity of tweets"

Connected through hyperedge HE-3 (induced by source paper "semantic variation in online communities of practice"):
  with members:
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "diffusion of lexical change in social media"
    - "efficient estimation of word representations in vector space"
    - "niche as a determinant of word fate in online groups"
    - "echoes of power language effects and power differences in social interaction"

Connected through hyperedge HE-4 (induced by source paper "life in the matrix human mobility patterns in the cyber space"):
  with members:
    - "community identity and user engagement in a multi community landscape"
    - "tales of two cities using social media to understand idiosyncratic lifestyles in distinctive metropolitan areas"
    - "loyalty in online communities"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "all who wander on the prevalence and characteristics of multi community engagement"

Connected through hyperedge HE-5 (induced by source paper "improving context aware language models"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "a persona based neural conversation model"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "towards a continuous modeling of natural language domains"
    - "generalizing and hybridizing count based and neural language models"
    - "using the output embedding to improve language models"
    - "context aware natural language generation with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "identifying the social signals that drive online discussions a case study of reddit communities"):
  with members:
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "trend of narratives in the age of misinformation"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "talking to the crowd what do people react to in online discussions"
    - "how to ask for a favor a case study on the success of altruistic requests"

Connected through hyperedge HE-7 (induced by source paper "the impact of crowds on news engagement a reddit case study"):
  with members:
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "talking to the crowd what do people react to in online discussions"
    - "a bayesian approach for predicting the popularity of tweets"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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)