human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "analysis of exact and approximated epidemic models over complex networks"
  Abstract: We study the spread of discrete-time epidemics over arbitrary networks for well-known propagation models, namely SIS (susceptible-infected-susceptible), SIR (susceptible-infected-recovered), SIRS (susceptible-infected-recovered-susceptible) and SIV (susceptible-infected-vaccinated). Such epidemics are described by $2^n$- or $3^n$-state Markov chains. Ostensibly, because analyzing such Markov chains is too complicated, their $O(n)$-dimensional nonlinear "mean-field" approximation, and its linearization, are often studied instead. We provide a complete global analysis of the epidemic dynamics of the nonlinear mean-field approximation. In particular, we show that depending on the largest eigenvalue of the underlying graph adjacency matrix and the rates of infection, recovery, and vaccination, the global dynamics takes on one of two forms: either the epidemic dies out, or it converges to another unique fixed point (the so-called endemic state where a constant fraction of the nodes remain infected). A similar result has also been shown in the continuous-time case. We tie in these results with the "true" underlying Markov chain model by showing that the linear model is the tightest upper-bound on the true probabilities of infection that involves only marginals, and that, even though the nonlinear model is not an upper-bound on the true probabilities in general, it does provide an upper-bound on the probability of the chain not being absorbed. As a consequence, we also show that when the disease-free fixed point is globally stable for the mean-field model, the Markov chain has an $O(\log n)$ mixing time, which means the epidemic dies out quickly. We compare and summarize the results on different propagation models.

Connected through hyperedge HE-1 (induced by source paper "transient dynamics of epidemic spreading and its mitigation on large networks"):
  with members:
    - "on the dynamics of deterministic epidemic propagation over networks"
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "optimal patching in clustered malware epidemics"
    - "optimal energy aware epidemic routing in dtns"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-2 (induced by source paper "game theoretic vaccination against networked sis epidemics and impacts of human decision making"):
  with members:
    - "game theoretic choice of curing rates against networked sis epidemics by human decision makers"
    - "designing virus resistant high performance networks a game formation approach"
    - "interdependent security games on networks under behavioral probability weighting"
    - "effects of degree correlations in interdependent security good or bad"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "fragility of the commons under prospect theoretic risk attitudes"
    - "networked sis epidemics with awareness"
    - "disease dynamics on a network game a little empathy goes a long way"

Connected through hyperedge HE-3 (induced by source paper "improved bounds on the epidemic threshold of exact sis models on complex networks"):
  with members:
    - "sirs epidemics on complex networks concurrence of exact markov chain and approximated models"
    - "information propagation speed in mobile and delay tolerant networks"

Question: Please tell me which class the center node 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)