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: "talking to the crowd what do people react to in online discussions"
  Abstract: This paper addresses the question of how language use affects community reaction to comments in online discussion forums, and the relative importance of the message vs. the messenger. A new comment ranking task is proposed based on community annotated karma in Reddit discussions, which controls for topic and timing of comments. Experimental work with discussion threads from six subreddits shows that the importance of different types of language features varies with the community of interest.

Connected through hyperedge HE-1 (induced by source paper "the anatomy of reddit an overview of academic research"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "the aftermath of disbanding an online hateful community"
    - "tracing community genealogy how new communities emerge from the old"
    - "community interaction and conflict on the web"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "on the origins of memes by means of fringe web communities"
    - "conversation modeling on reddit using a graph structured lstm"
    - "identifying the social signals that drive online discussions a case study of reddit communities"

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"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "a bayesian approach for predicting the popularity of tweets"

Connected through hyperedge HE-3 (induced by source paper "a study of question effectiveness using reddit ask me anything threads"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "a neural attention model for abstractive sentence summarization"
    - "show attend and tell neural image caption generation with visual attention"
    - "molding cnns for text non linear non consecutive convolutions"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "using sentiment induction to understand variation in gendered online communities"):
  with members:
    - "community identity and user engagement in a multi community landscape"
    - "loyalty in online communities"
    - "ultradense word embeddings by orthogonal transformation"
    - "gender identity and lexical variation in social media"
    - "how to ask for a favor a case study on the success of altruistic requests"

Connected through hyperedge HE-5 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "conversation modeling on reddit using a graph structured lstm"):
  with members:
    - "learning latent local conversation modes for predicting community endorsement in online discussions"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "recurrent neural network grammars"
    - "theano a python framework for fast computation of mathematical expressions"
    - "compositional distributional semantics with long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-7 (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"
    - "characterizing the language of online communities and its relation to community reception"
    - "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"
    - "how to ask for a favor a case study on the success of altruistic requests"

Connected through hyperedge HE-8 (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"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "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)