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: "exploring text virality in social networks"
  Abstract: This paper aims to shed some light on the concept of virality - especially in social networks - and to provide new insights on its structure. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread, rather than to the influencers who spread it, (b) virality is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised and they only partially overlap. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be independently predicted according to content features.

Connected through hyperedge HE-1 (induced by source paper "causal effects of brevity on style and success in social media"):
  with members:
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "tgsum build tweet guided multi document summarization dataset"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "a computational approach to politeness with application to social factors"
    - "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia"

Connected through hyperedge HE-2 (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-3 (induced by source paper "viral content propagation in online social networks"):
  with members:
    - "the role of social networks in information diffusion"
    - "topic diffusion and emergence of virality in social networks"

Connected through hyperedge HE-4 (induced by source paper "why do urban legends go viral"):
  with members:
    - "assessing sentiment strength in words prior polarities"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "you had me at hello how phrasing affects memorability"
    - "finding deceptive opinion spam by any stretch of the imagination"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "analysis of social voting patterns on digg"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-5 (induced by source paper "prediction and characterization of high activity events in social media triggered by real world news"):
  with members:
    - "codebook based audio feature representation for music information retrieval"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-6 (induced by source paper "on the role of conductance geography and topology in predicting hashtag virality"):
  with members:
    - "can cascades be predicted"
    - "a bayesian approach for predicting the popularity of tweets"
    - "predicting successful memes using network and community structure"
    - "exploring image virality in google plus"
    - "virality prediction and community structure in social networks"
    - "topic diffusion and emergence of virality in social networks"
    - "information diffusion and external influence in networks"
    - "good friends bad news affect and virality in twitter"

Connected through hyperedge HE-7 (induced by source paper "deep feelings a massive cross lingual study on the relation between emotions and virality"):
  with members:
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "anger is more influential than joy sentiment correlation in weibo"
    - "the role of emotions in propagating brands in social networks"
    - "how to ask for a favor a case study on the success of altruistic requests"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "exploring image virality in google plus"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "how the scientific community reacts to newly submitted preprints article downloads twitter mentions and citations"

Connected through hyperedge HE-8 (induced by source paper "all who wander on the prevalence and characteristics of multi community engagement"):
  with members:
    - "evolution of reddit from the front page of the internet to a self referential community"
    - "how community feedback shapes user behavior"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "you had me at hello how phrasing affects memorability"
    - "good friends bad news affect and virality in twitter"
    - "on the interplay between social and topical structure"
    - "mark my words linguistic style accommodation in social media"

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)