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 "optimal deployment of resources for maximizing impact in spreading processes"):
  Abstract: The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of "influential spreaders" for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings the problem is often characterized by heterogeneous interactions and requires interventions over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.

Includes members:
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "reconstructing parameters of spreading models from partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detecting social influence in event cascades by comparing discriminative rankers")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seeding with costly network information")
  Member "optimizing active cyber defense":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metrics towards measuring cyber agility")
  Member "detection of cyber physical faults and intrusions from physical correlations":
    also connected through other hyperedges:
      (none)
  Member "an efficient curing policy for epidemics on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "locating the source of diffusion in large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noisy source location on a line")

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)