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: "early warning analysis for social diffusion events"
  Abstract: There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks.

Connected through hyperedge HE-1 (induced by source paper "applying social media intelligence for predicting and identifying on line radicalization and civil unrest oriented threats"):
  with members:
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "uncovering the wider structure of extreme right communities spanning popular online networks"
    - "solutions to detect and analyze online radicalization a survey"
    - "an analysis of interactions within and between extreme right communities in social media"

Connected through hyperedge HE-2 (induced by source paper "predicting successful memes using network and community structure"):
  with members:
    - "can cascades be predicted"
    - "the digital evolution of occupy wall street"
    - "the role of information diffusion in the evolution of social networks"
    - "virality prediction and community structure in social networks"
    - "the pulse of news in social media forecasting popularity"
    - "exploring text virality in social networks"
    - "on the interplay between social and topical structure"
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-3 (induced by source paper "social dynamics of digg"):
  with members:
    - "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"
    - "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"
    - "analysis of social voting patterns on digg"
    - "novelty and collective attention"

Connected through hyperedge HE-4 (induced by source paper "leveraging sociological models for predictive analytics"):
  with members:
    - "web analytics for security informatics"
    - "twitter mood predicts the stock market"
    - "using stochastic models to describe and predict social dynamics of web users"

Connected through hyperedge HE-5 (induced by source paper "predictive non equilibrium social science"):
  with members:
    - "web analytics for security informatics"

Connected through hyperedge HE-6 (induced by source paper "web analytics for security informatics"):
  with members:
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-7 (induced by source paper "using stochastic models to describe and predict social dynamics of web users"):
  with members:
    - "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"
    - "stochastic models of user contributory web sites"
    - "predicting the popularity of online content"
    - "analysis of social voting patterns on digg"
    - "novelty and collective attention"
    - "social information processing in news aggregation"
    - "social browsing on flickr"

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.SI(Social and Information Networks)