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 "a variable density sampling scheme for compressive fourier transform interferometry"):
  Abstract: Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality in that applications demand high spectral resolution such as fluorescence microscopy. However, the effective resolution of FTI is indeed limited due to the durability of biological elements when exposed to illuminating light, since over-exposed elements become unable to fluoresce, i.e., photo-bleaching phenomenon. In this context, the acquisition of biological HS volumes based on sampling the Optical Path Difference (OPD) axis at Nyquist rate leads to unpleasant trade-offs between spectral resolution, quality of the HS volume, and light exposure intensity. In this paper we propose two variants of the FTI imager, i.e., Coded Illumination-FTI (CI-FTI) and Coded Aperture FTI (CA-FTI), based on the theory of compressed sensing. These schemes efficiently modulate light exposure temporally (in CI-FTI) or spatio-temporally (in CA-FTI). Leveraging a variable density sampling strategy, we provide optimal illumination and aperture coding strategies, so that the light exposure imposed on a biological specimen is minimized while the spectral resolution is preserved. Our theoretical analysis consists of two parts: (i) the trade-off between exposure intensity and the quality of the acquired HS volume for a fixed spectral resolution; (ii) the best HS volume quality for a fixed spectral resolution and constrained exposure intensity budget. Our contributions can be adapted to an FTI imager without hardware modifications. The reconstruction of HS volumes from compressive sensing FTI measurements relies on an $\ell_1$-norm minimization problem promoting a 3D sparsity prior. Numerically, we support the proposed methods on synthetic and experimental FTI data under various scenarios. In particular, the biological HS volume can be reconstructed with a three-to ten-fold reduction in the exposure intensity.

Includes members:
  Member "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "compressive single pixel fourier transform imaging using structured illumination":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "on oracle type local recovery guarantees in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "multispectral compressive imaging strategies using fabry p erot filtered sensors":
    also connected through other hyperedges:
      (none)
  Member "compressed sensing with structured sparsity and structured acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "breaking the coherence barrier a new theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "an analysis of block sampling strategies in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "a panorama on multiscale geometric representations intertwining spatial directional and frequency selectivity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "state of the art in retinal optical coherence tomography image analysis")

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)