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 "link delay estimation using sparse recovery for dynamic network tomography"):
  Abstract: When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state parameters of network links, e.g., transmission delays and dropped packet rates, because such monitoring operations are usually time-consuming. Based on the sparse recovery technique reported in [Wang et al. (2015) IEEE Trans. Information Theory, 61(2):1028--1044], which can infer link delays from a limited number of measurements using compressed sensing, we particularly extend to networks with dynamic changes including link insertion and deletion. Moreover, we propose a more efficient algorithm with a better theoretical upper bound. The experimental result also demonstrates that our algorithm outperforms the previous work in running time while maintaining a similar recovery performance, which shows its capability to cope with large-scale networks.

Includes members:
  Member "link delay estimation via expander graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing in multi hop large scale wireless sensor networks based on routing topology tomography")
  Member "compressive sensing over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural network tomography")

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.NI(Networking and Internet Architecture)