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: "using stochastic models to describe and predict social dynamics of web users"
  Abstract: Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions between content quality and how the social media site chooses to highlight content. Moreover, most social media sites also selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity \emph{a priori}, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions based on simply extrapolating from the early votes. Using data from one such site, the news aggregator Digg, we show how a stochastic model of user behavior distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, identifying stories primarily of interest to users in the network (``niche interests'') from those of more general interest to the user community. This distinction is useful for predicting a story's eventual popularity from users' early reactions to the story.

Connected through hyperedge HE-1 (induced by source paper "continuous time user modeling in the presence of badges a probabilistic approach"):
  with members:
    - "spatio temporal modeling of users check ins in location based social networks"
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "engaging with massive online courses"
    - "stochastic models predict user behavior in social media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-2 (induced by source paper "the impact of the filter bubble a simulation based framework for measuring personalisation macro effects in online communities"):
  with members:
    - "social dynamics of digg"

Connected through hyperedge HE-3 (induced by source paper "modeling and predicting popularity dynamics via reinforced poisson processes"):
  with members:
    - "modeling information propagation with survival theory"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "quantifying long term scientific impact"
    - "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-4 (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"
    - "stochastic models of user contributory web sites"
    - "predicting the popularity of online content"
    - "analysis of social voting patterns on digg"
    - "social information processing in news aggregation"

Connected through hyperedge HE-5 (induced by source paper "rethinking centrality the role of dynamical processes in social network analysis"):
  with members:
    - "the simple rules of social contagion"
    - "social dynamics of digg"
    - "what stops social epidemics"
    - "entropy based classification of retweeting activity on twitter"
    - "parameterized centrality metric for network analysis"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "a framework for quantitative analysis of cascades on networks"
    - "influence and passivity in social media"

Connected through hyperedge HE-6 (induced by source paper "social dynamics of digg"):
  with members:
    - "early warning analysis for social diffusion events"
    - "folks in folksonomies social link prediction from shared metadata"
    - "using a model of social dynamics to predict popularity of news"
    - "supervised random walks predicting and recommending links in social networks"
    - "stochastic models of user contributory web sites"
    - "predicting the popularity of online content"
    - "analysis of social voting patterns on digg"
    - "novelty and collective attention"

Connected through hyperedge HE-7 (induced by source paper "leveraging sociological models for predictive analytics"):
  with members:
    - "early warning analysis for social diffusion events"
    - "web analytics for security informatics"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-8 (induced by source paper "early warning analysis for social diffusion events"):
  with members:
    - "web analytics for security informatics"
    - "twitter mood predicts the stock market"
    - "a model of internet topology using k shell decomposition"

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)