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 "cascading behavior in yelp reviews"):
  Abstract: Social media has changed the landscape of marketing and consumer research as the adoption and promotion of businesses is becoming more and more dependent on how the customers are interacting and feeling about the business on platforms like Facebook, Twitter, Yelp etc. Social review websites like Yelp have become an important source of information about different businesses. Social influence on these online platforms can result in individuals adopting or promoting ideas and actions resulting in information cascades. Research on information cascades have been gaining popularity over the last few years but most of the research has been focused on platforms like Twitter and Facebook. Research on the adoption or promotion of product using cascades can help determine important latent patterns of social influence. #R##N#In this work, we have analyzed the spread of information i.e. cascades in Yelp across different cities in Europe and North America. We have enumerated and analyzed different cascade topologies that occur in the Yelp social networks. Some of our significant findings include the presence of a significant number of cascades in Yelp reviews indicating the importance of social influence, heavy-tailed distribution of cascades and possibility to accurately predict the size of cascades on the basis of initial reviews. In addition, we have also found that the characteristics of the non-root nodes and the non-root reviews are much more important type of feature as compared to the properties of the root nodes and root reviews of the cascade. #R##N#These findings can help social scientists to analyze customer behavior across different cities in a much more systematic way. Furthermore, it can also help the businesses in a city to figure out different consumer trends and hence improve their processes and offerings.

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 "on the interplay between social and topical structure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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)