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: "analysis of social voting patterns on digg"
  Abstract: The social Web is transforming the way information is created and distributed. Blog authoring tools enable users to publish content, while sites such as Digg and Del.icio.us are used to distribute content to a wider audience. With content fast becoming a commodity, interest in using social networks to promote and find content has grown, both on the side of content producers (viral marketing) and consumers (recommendation). Here we study the role of social networks in promoting content on Digg, a social news aggregator that allows users to submit links to and vote on news stories. Digg's goal is to feature the most interesting stories on its front page, and it aggregates opinions of its many users to identify them. Like other social networking sites, Digg allows users to designate other users as ``friends'' and see what stories they found interesting. We studied the spread of interest in news stories submitted to Digg in June 2006. Our results suggest that pattern of the spread of interest in a story on the network is indicative of how popular the story will become. Stories that spread mainly outside of the submitter's neighborhood go on to be very popular, while stories that spread mainly through submitter's social neighborhood prove not to be very popular. This effect is visible already in the early stages of voting, and one can make a prediction about the potential audience of a story simply by analyzing where the initial votes come from.

Connected through hyperedge HE-1 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks"):
  with members:
    - "graph wavelet neural network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-2 (induced by source paper "multi source social feedback of online news feeds"):
  with members:
    - "predictability of popularity gaps between prediction and understanding"
    - "a bayesian approach for predicting the popularity of tweets"
    - "the pulse of news in social media forecasting popularity"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "predictability of popularity gaps between prediction and understanding"):
  with members:
    - "distinguishing between personal preferences and social influence in online activity feeds"
    - "studying and modeling the connection between people s preferences and content sharing"
    - "the simple rules of social contagion"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "virality prediction and community structure in social networks"
    - "the pulse of news in social media forecasting popularity"

Connected through hyperedge HE-4 (induced by source paper "why do urban legends go viral"):
  with members:
    - "exploring text virality in social networks"
    - "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"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-5 (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"
    - "exploring text virality in social networks"

Connected through hyperedge HE-6 (induced by source paper "altmetrics chapter from beyond bibliometrics harnessing multidimensional indicators of scholarly impact"):
  with members:
    - "tracing scientist s research trends realtimely"
    - "how does market concern derived from the internet affect oil prices"
    - "how the scientific community reacts to newly submitted preprints article downloads twitter mentions and citations"
    - "beyond citations scholars visibility on the social web"
    - "altmetrics in the wild using social media to explore scholarly impact"
    - "twitter mood predicts the stock market"
    - "a principal component analysis of 39 scientific impact measures"
    - "scientific citations in wikipedia"

Connected through hyperedge HE-7 (induced by source paper "stochastic models predict user behavior in social media"):
  with members:
    - "friendship paradox redux your friends are more interesting than you"
    - "collaborative topic regression with social matrix factorization for recommendation systems"
    - "social dynamics of digg"
    - "using stochastic models to describe and predict social dynamics of web users"
    - "stochastic models of user contributory web sites"
    - "predicting the popularity of online content"
    - "social information processing in news aggregation"

Connected through hyperedge HE-8 (induced by source paper "exploring image virality in google plus"):
  with members:
    - "exploring text virality in social networks"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "you had me at hello how phrasing affects memorability"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

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.CY(Computers and Society)