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 "back to the source an online approach for sensor placement and source localization"):
  Abstract: Source localization, the act of finding the originator of a disease or rumor in a network, has become an important problem in sociology and epidemiology. The localization is done using the infection state and time of infection of a few designated sensor nodes; however, maintaining sensors can be very costly in practice. #R##N#We propose the first online approach to source localization: We deploy a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively choose the best location to place new sensors in order to localize the source. This approach allows for source localization with a very small number of sensors; moreover, the source can be found while the epidemic is still ongoing. Our method applies to a general network topology and performs well even with random transmission delays.

Includes members:
  Member "observer placement for source localization the effect of budgets and transmission variance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metric dimension")
  Member "back to the past source identification in diffusion networks from partially observed cascades":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascade size distributions and why they matter")
  Member "identification of source of rumors in social networks with incomplete information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "observer placement for source localization the effect of budgets and transmission variance")
  Member "approximability of the minimum weighted doubly resolving set problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "observer placement for source localization the effect of budgets and transmission variance")
  Member "how to identify an infection source with limited observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate identification of the optimal epidemic source in complex networks")
  Member "a robust information source estimator with sparse observations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information source detection with limited time knowledge")
  Member "rooting out the rumor culprit from suspects":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "necessary and sufficient budgets in information source finding with querying adaptivity gap")
  Member "information source detection in the sir model a sample path based approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media")

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)