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 "latent viral marketing concepts and control methods"):
  Abstract: Numerus works that study the spread of information in social networks include a spreading mechanism in which a set of nodes is initially infected (i.e. seeded), followed by a viral process, which spontaneously spread the message through the nodes of the network. These models are used to describe the spread of rumors as well as the spread of new products and services. In reality however, it is quite rare that a product or service spreads through a social networks solely by viral forces. It is more common, that the spreader would invests a continuous active effort of its sales representatives in order to enhance the spread. The Latent Viral Marketing Model is a spreading model that fits this reality. Along the description of the model, the paper continues and proposes a simple Scheduling of Seeding heuristics, which recommends the node to seed at each period. A large study of empirical simulations shows that under a wide range of realistic initial conditions, the Scheduling Seeding method improves a product adoption rate by 29%-94% in comparison to existing state-of-the-art seeding methods.

Includes members:
  Member "information is not a virus and other consequences of human cognitive limits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the opacity problem in social contagion")
  Member "sequential seeding in complex networks trading speed for coverage":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "influence maximization for fixed heterogeneous thresholds")
  Member "a scalable heuristic for viral marketing under the tipping model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "influence maximization for fixed heterogeneous thresholds")
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")

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)