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 "network topology inference using information cascades with limited statistical knowledge"):
  Abstract: We study the problem of inferring network topology from information cascades, in which the amount of time taken for information to diffuse across an edge in the network follows an unknown distribution. Unlike previous studies, which assume knowledge of these distributions, we only require that diffusion along different edges in the network be independent together with limited moment information (e.g., the means). We introduce the concept of a separating vertex set for a graph, which is a set of vertices in which for any two given distinct vertices of the graph, there exists a vertex whose distance to them are different. We show that a necessary condition for reconstructing a tree perfectly using distance information between pairs of vertices is given by the size of an observed separating vertex set. We then propose an algorithm to recover the tree structure using infection times, whose differences have means corresponding to the distance between two vertices. To improve the accuracy of our algorithm, we propose the concept of redundant vertices, which allows us to perform averaging to better estimate the distance between two vertices. Though the theory is developed mainly for tree networks, we demonstrate how the algorithm can be extended heuristically to general graphs. Simulation results suggest that our proposed algorithm performs better than some current state-of-the-art network reconstruction methods.

Includes members:
  Member "estimating infection sources in networks using partial timestamps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information source detection with limited time knowledge")
  Member "back to the source an online approach for sensor placement and source localization":
    also connected through other hyperedges:
      (none)
  Member "network topology inference from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "infection spreading and source identification a hide and seek game":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating infection sources in networks using partial timestamps")
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "whose opinion to follow in multihypothesis social learning a large deviations perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "learning laplacian matrix in smooth graph signal representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "how to identify an infection source with limited observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate identification of the optimal epidemic source in complex 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)