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 "stability of sis spreading processes in networks with non markovian transmission and recovery"):
  Abstract: Although viral spreading processes taking place in networks are often analyzed using Markovian models in which both the transmission and the recovery times follow exponential distributions, empirical studies show that, in many real scenarios, the distribution of these times are not necessarily exponential. To overcome this limitation, we first introduce a generalized susceptible-infected-susceptible (SIS) spreading model that allows transmission and recovery times to follow phase-type distributions. In this context, we derive a lower bound on the exponential decay rate towards the infection-free equilibrium of the spreading model without relying on mean-field approximations. Based on our results, we illustrate how the particular shape of the transmission/recovery distribution influences the exponential rate of convergence towards the equilibrium.

Includes members:
  Member "second order moment closure for tighter epidemic thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the dynamics of deterministic epidemic propagation over networks")
  Member "optimal containment of epidemics in temporal and adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "second order moment closure for tighter epidemic thresholds")
  Member "stability of spreading processes over time varying large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient containment of exact sir markovian processes on networks")
  Member "epidemic processes over adaptive state dependent networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient containment of exact sir markovian processes on networks")
  Member "improved bounds on the epidemic threshold of exact sis models on complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "second order moment closure for tighter epidemic thresholds")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)