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 "estimating infection sources in networks using partial timestamps"):
  Abstract: We study the problem of identifying infection sources in a network based on the network topology, and a subset of infection timestamps. In the case of a single infection source in a tree network, we derive the maximum likelihood estimator of the source and the unknown diffusion parameters. We then introduce a new heuristic involving an optimization over a parametrized family of Gromov matrices to develop a single source estimation algorithm for general graphs. Compared with the breadth-first search tree heuristic commonly adopted in the literature, simulations demonstrate that our approach achieves better estimation accuracy than several other benchmark algorithms, even though these require more information like the diffusion parameters. We next develop a multiple sources estimation algorithm for general graphs, which first partitions the graph into source candidate clusters, and then applies our single source estimation algorithm to each cluster. We show that if the graph is a tree, then each source candidate cluster contains at least one source. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithms are able to estimate the true infection source(s) to within a small number of hops with a small portion of the infection timestamps being observed.

Includes members:
  Member "optimal localization of diffusion sources in complex networks":
    also connected through other hyperedges:
      (none)
  Member "modeling the propagation of trojan malware in online social networks":
    also connected through other hyperedges:
      (none)
  Member "infection spreading and source identification a hide and seek game":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network topology inference using information cascades with limited statistical knowledge")
  Member "source localization in networks trees and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "catch em all locating multiple diffusion sources in networks with partial observations")
  Member "how to identify an infection source with limited observations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate identification of the optimal epidemic source in complex networks")
  Member "spy vs spy rumor source obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "identifying infection sources and regions in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gfcn a new graph convolutional network based on parallel flows")
  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")

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)