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 "the cost of uncertainty in curing epidemics"):
  Abstract: Motivated by the study of controlling (curing) epidemics, we consider the spread of an SI process on a known graph, where we have a limited budget to use to transition infected nodes back to the susceptible state (i.e., to cure nodes). Recent work has demonstrated that under perfect information (which nodes are/are not infected), the budget required for curing a graph precisely depends on its CutWidth. We develop a model for observation uncertainty, and show that even a minor degradation in the assumption of perfect knowledge of the state of the infection drastically alters the landscape -- infections that could previously be cured in sublinear time, now may require exponential time to cure. Our main result characterizes the necessary curing budget required as a function of the per-node observation uncertainty.

Includes members:
  Member "a lower bound on the performance of dynamic curing policies for epidemics on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "when is a network epidemic hard to eliminate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient containment of exact sir markovian processes on networks")
  Member "spy vs spy rumor source obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "an efficient curing policy for epidemics on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "distinguishing infections on different graph topologies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detecting weak changes in dynamic events over networks")

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)