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 "information source detection with limited time knowledge"):
  Abstract: This paper investigates the problem of utilizing network topology and partial timestamps to detect the information source in a network. The problem incurs prohibitive cost under canonical maximum likelihood estimation (MLE) of the source due to the exponential number of possible infection paths. Our main idea of source detection, however, is to approximate the MLE by an alternative infection path based estimator, the essence of which is to identify the most likely infection path that is consistent with observed timestamps. The source node associated with that infection path is viewed as the estimated source $\hat{v}$. We first study the case of tree topology, where by transforming the infection path based estimator into a linear integer programming, we find a reduced search region that remarkably improves the time efficiency. Within this reduced search region, the estimator $\hat{v}$ is provably always on a path which we term as \emph{candidate path}. This notion enables us to analyze the distribution of $d(v^{\ast},\hat{v})$, the error distance between $\hat{v}$ and the true source $v^{\ast}$, on arbitrary tree, which allows us to obtain for the first time, in the literature provable performance guarantee of the estimator under limited timestamps. Specifically, on the infinite $g$-regular tree with uniform sampled timestamps, we get a refined performance guarantee in the sense of a constant bounded $d(v^{\ast},\hat{v})$. By virtue of time labeled BFS tree, the estimator still performs fairly well when extended to more general graphs. Experiments on both synthetic and real datasets further demonstrate the superior performance of our proposed algorithms.

Includes members:
  Member "on the universality of jordan centers for estimating infection sources in tree networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gfcn a new graph convolutional network based on parallel flows")
  Member "estimating infection sources in networks using partial timestamps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "network topology inference using information cascades with limited statistical knowledge")
  Member "catch em all locating multiple diffusion sources in networks with partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascade size distributions and why they matter")
  Member "multiple infection sources identification with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate identification of the optimal epidemic source in complex networks")
  Member "hiding the rumor source":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "how to identify an infection source with limited observations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "dandelion lightweight cryptocurrency networking with formal anonymity guarantees")
  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)