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 "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"):
  Abstract: We investigate the problems of 1-D and 2-D signal recovery from subsampled Hadamard measurements using Haar wavelet sparsity prior. These problems are of interest in, e.g., computational imaging applications relying on optical multiplexing or single-pixel imaging. However, the realization of such modalities is often hindered by the coherence between the Hadamard and Haar bases. The variable and multilevel density sampling strategies solve this issue by adjusting the subsampling process to the local and multilevel coherence, respectively, between the two bases; hence enabling successful signal recovery. In this work, we compute an explicit sample-complexity bound for Hadamard-Haar systems as well as uniform and non-uniform recovery guarantees; a seemingly missing result in the related literature. We explore the faithfulness of the numerical simulations to the theoretical results and show in a practically relevant instance, e.g., single-pixel camera, that the target signal can be obtained from a few Hadamard measurements.

Includes members:
  Member "linear reconstructions and the analysis of the stable sampling rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non uniform recovery guarantees for binary measurements and infinite dimensional compressed sensing")
  Member "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a variable density sampling scheme for compressive fourier transform interferometry")
  Member "compressive single pixel fourier transform imaging using structured illumination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a variable density sampling scheme for compressive fourier transform interferometry")
  Member "compressive hyperspectral imaging fourier transform interferometry meets single pixel camera":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive single pixel fourier transform imaging using structured illumination")
  Member "on oracle type local recovery guarantees in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing")
  Member "compressive imaging using fast transform coding":
    also connected through other hyperedges:
      (none)
  Member "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")

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.IT(Information Theory)