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 "on the universality of jordan centers for estimating infection sources in tree networks"):
  Abstract: Finding the infection sources in a network when we only know the network topology and infected nodes, but not the rates of infection, is a challenging combinatorial problem, and it is even more difficult in practice where the underlying infection spreading model is usually unknown  a priori . In this paper, we are interested in finding a source estimator that is applicable to various spreading models, including the susceptible–infected (SI), susceptible–infected–recovered (SIR), susceptible–infected–recovered–infected (SIRI), and susceptible–infected–susceptible (SIS) models. We show that under the SI, SIR, and SIRI spreading models and with mild technical assumptions, the Jordan center is the infection source associated with the most likely infection path in a tree network with a single infection source. This conclusion applies for a wide range of spreading parameters, while it holds for regular trees under the SIS model with homogeneous infection and recovery rates. Since the Jordan center does not depend on the infection, recovery, and reinfection rates, it can be regarded as a  universal  source estimator. We also consider the case where there are    $k>1$    infection sources, generalize the Jordan center definition to a    $k$   -Jordan center set, and show that this is an optimal infection source set estimator in a tree network for the SI model. Simulation results on various general synthetic networks and real-world networks suggest that Jordan center-based estimators consistently outperform the betweenness, closeness, distance, degree, eigenvector, and pagerank centrality-based heuristics, even if the network is not a tree.

Includes members:
  Member "how to identify an infection source with limited observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate identification of the optimal epidemic source in complex networks")
  Member "identifying infection sources and regions in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (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)