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: "cumulative effect in information diffusion empirical study on a microblogging network"
  Abstract: Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.

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"
    - "what stops social epidemics"
    - "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 "impact of social influence on adoption behavior an online controlled experimental evaluation"):
  with members:
    - "information diffusion and external influence in networks"
    - "what stops social epidemics"

Connected through hyperedge HE-3 (induced by source paper "weseer visual analysis for better information cascade prediction of wechat articles"):
  with members:
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "the pulse of news in social media forecasting popularity"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-4 (induced by source paper "modelling of information diffusion on social networks with applications to wechat"):
  with members:
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "do cascades recur"
    - "the role of social networks in information diffusion"
    - "a framework for quantitative analysis of cascades on networks"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "toward early and order of magnitude cascade prediction in social networks"):
  with members:
    - "toward order of magnitude cascade prediction"
    - "predicting successful memes using network and community structure"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "competition between homophily and information entropy maximization in social networks"):
  with members:
    - "estimating and sampling graphs with multidimensional random walks"
    - "weak ties subtle role of information diffusion in online social networks"
    - "distance is not dead social interaction and geographical distance in the internet era"
    - "rhythms of social interaction messaging within a massive online network"

Connected through hyperedge HE-7 (induced by source paper "who creates trends in online social media the crowd or opinion leaders"):
  with members:
    - "online popularity and topical interests through the lens of instagram"
    - "evolution of online user behavior during a social upheaval"
    - "clustering memes in social media streams"
    - "the digital evolution of occupy wall street"
    - "traveling trends social butterflies or frequent fliers"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "virality prediction and community structure in social networks"
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-8 (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"
    - "quantifying long term scientific impact"
    - "using a model of social dynamics to predict popularity of news"
    - "using stochastic models to describe and predict social dynamics of web users"
    - "predicting the popularity of online content"
    - "novelty and collective attention"

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)