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 "modeling non progressive phenomena for influence propagation"):
  Abstract: Recent work on modeling influence propagation focus on progressive models, i.e., once a node is influenced (active) the node stays in that state and cannot become inactive. However, this assumption is unrealistic in many settings where nodes can transition between active and inactive states. For instance, a user of a social network may stop using an app and become inactive, but again activate when instigated by a friend, or when the app adds a new feature or releases a new version. In this work, we study such non-progressive phenomena and propose an efficient model of influence propagation. Specifically, we model in influence propagation as a continuous-time Markov process with 2 states: active and inactive. Such a model is both highly scalable (we evaluated on graphs with over 2 million nodes), 17-20 times faster, and more accurate for estimating the spread of influence, as compared with state-of-the-art progressive models for several applications where nodes may switch states.

Includes members:
  Member "scalable influence estimation in continuous time diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate k cover in hypergraphs efficient algorithms and applications")
  Member "influence maximization in continuous time diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "a data based approach to social influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making")
  Member "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discovering polarized communities in signed networks")
  Member "on the non progressive spread of influence through social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "whom to befriend to influence people")

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)