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 "evidential positive opinion influence measures for viral marketing"):
  Abstract: The viral marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles three opinion-based scenarios for viral marketing in the real world. The first scenario concerns influencers who have a positive opinion about the product. The second scenario deals with influencers who have a positive opinion about the product and produces effects on users who also have a positive opinion. The third scenario involves influence users who have a positive opinion about the product and produce effects on the negative opinion of other users concerning the product in question. Next, we proposed six influence measures, two for each scenario. We also use an influence maximization model that the set of detected influencers for each scenario. Finally, we show the performance of the proposed model with each influence measure through some experiments conducted on a generated dataset and a real-world dataset collected from Twitter.

Includes members:
  Member "two evidential data based models for influence maximization in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "independence of sources in social networks")
  Member "maximizing positive opinion influence using an evidential approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a reliability based approach for influence maximization using the evidence theory")
  Member "median evidential c means algorithm and its application to community detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "calibrated model based evidential clustering using bootstrapping")
  Member "classification of message spreading in a heterogeneous social network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "two evidential data based models for influence maximization in twitter")
  Member "dynamic time warping distance for message propagation classification in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a reliability based approach for influence maximization using the evidence theory")
  Member "a data based approach to social influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making")

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)