human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "compressive single pixel fourier transform imaging using structured illumination"):
  with members:
    - "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"
    - "compressive hyperspectral imaging fourier transform interferometry meets single pixel camera"
    - "compressive sampling approach for image acquisition with lensless endoscope"
    - "compressive imaging and characterization of sparse light deflection maps"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-2 (induced by source paper "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"):
  with members:
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-3 (induced by source paper "compressive hyperspectral imaging fourier transform interferometry meets single pixel camera"):
  with members:
    - "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"
    - "breaking the coherence barrier a new theory for compressed sensing"

Question: Please tell me which class the center node 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)