human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "understanding spreading patterns on social networks based on network topology"):
  Abstract: Ever since the proposal of first epidemic model, scientists have been attempting to estimate the growth of a disease/contagion while in its premature stage. Despite being the focus of researchers for a long time, understanding epidemiology remains as error prone as a weather forecast, mainly because of the unavailability of large amount of data. An epidemic spread is analogous to the diffusion of memes in social networking sites. Diffusion of memes can be easily studied provided large datasets and computational powers to extract information from online networks. So, studying a meme spreading pattern can help us in understanding epidemiology. In this paper, we analyse the impact of the topology of a social network, specifically its meso scale properties- community structure and core-periphery structure, on a meme traversing over it. We propose a meme propagation model for synthetic scale free graphs which resemble real world graphs and observe the process of a meme going viral on such a network. We also validate our model for real world information propagation.

Includes members:
  Member "can cascades be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "exploring text virality in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causal effects of brevity on style and success in social media")
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")

Question: Please tell me which class the source paper 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)