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: "what stops social epidemics"
  Abstract: Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.

Connected through hyperedge HE-1 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making"):
  with members:
    - "leveraging motifs to model the temporal dynamics of diffusion networks"
    - "impact of social influence on adoption behavior an online controlled experimental evaluation"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "co evolution of selection and influence in social networks"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-2 (induced by source paper "modeling information diffusion in online social networks with partial differential equations"):
  with members:
    - "information diffusion and external influence in networks"
    - "diffusive logistic model towards predicting information diffusion in online social networks"
    - "on the interplay between social and topical structure"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "using network motifs to characterize temporal network evolution leading to diffusion inhibition"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "understanding and forecasting lifecycle events in information cascades"
    - "higher order organization of complex networks"
    - "do cascades recur"
    - "toward early and order of magnitude cascade prediction in social networks"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"

Connected through hyperedge HE-4 (induced by source paper "the opacity problem in social contagion"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "node2vec scalable feature learning for networks"
    - "can cascades be predicted"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "distributed representations of words and phrases and their compositionality"
    - "virality prediction and community structure in social networks"
    - "social influence in social advertising evidence from field experiments"

Connected through hyperedge HE-5 (induced by source paper "impact of social influence on adoption behavior an online controlled experimental evaluation"):
  with members:
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "information diffusion and external influence in networks"

Connected through hyperedge HE-6 (induced by source paper "can you trust the trend discovering simpson s paradoxes in social data"):
  with members:
    - "on quitting performance and practice in online game play"
    - "information is not a virus and other consequences of human cognitive limits"
    - "evidence of online performance deterioration in user sessions on reddit"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "the simple rules of social contagion"

Connected through hyperedge HE-7 (induced by source paper "predicting and explaining behavioral data with structured feature space decomposition"):
  with members:
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "the mythos of model interpretability"
    - "the simple rules of social contagion"
    - "popularity and quality in social news aggregators a study of reddit and hacker news"
    - "rule based machine learning methods for functional prediction"

Connected through hyperedge HE-8 (induced by source paper "computational social scientist beware simpson s paradox in behavioral data"):
  with members:
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "dynamics of content quality in collaborative knowledge production"
    - "information is not a virus and other consequences of human cognitive limits"
    - "evidence of online performance deterioration in user sessions on reddit"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "the simple rules of social contagion"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "social dynamics of digg"

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)