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: "interdependent security with strategic agents and cascades of infection"
  Abstract: We investigate cascades in networks consisting of strategic agents with interdependent security. We assume that the strategic agents have choices between i) investing in protecting themselves, ii) purchasing insurance to transfer (some) risks, and iii) taking no actions. Using a population game model, we study how various system parameters, such as node degrees, infection propagation rate, and the probability with which infected nodes transmit infection to neighbors, affect nodes' choices at Nash equilibria and the resultant price of anarchy/stability. In addition, we examine how the probability that a single infected node can spread the infection to a significant portion of the entire network, called cascade probability, behaves with respect to system parameters. In particular, we demonstrate that, at least for some parameter regimes, the cascade probability increases with the average degree of nodes.

Connected through hyperedge HE-1 (induced by source paper "interdependent security games on networks under behavioral probability weighting"):
  with members:
    - "fragility of the commons under prospect theoretic risk attitudes"
    - "stochastic games for security in networks with interdependent nodes"
    - "decision and game theory for security"

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"
    - "analysis of exact and approximated epidemic models over complex networks"
    - "networked sis epidemics with awareness"

Connected through hyperedge HE-3 (induced by source paper "effects of degree correlations in interdependent security good or bad"):
  with members:
    - "internalization of externalities in interdependent security large network cases"
    - "hiding the rumor source"
    - "a local mean field analysis of security investments in 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)