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 parallel compressive imaging architecture for one shot acquisition"
  Abstract: A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than the number of reconstructed pixels and is able to acquire the image in a single acquisition. This paves the way for the development of video architectures that acquire several frames per second. We specifically address the diffraction problem, showing that deconvolution normally used to recover diffraction blur can be replaced by convolution of the sensing matrix, and how measurements of a 0/1 physical sensing matrix can be converted to -1/1 compressive sensing matrix without any extra acquisitions. Simulations of our architecture show that the image quality is comparable to that of a classic Compressive Imaging camera, whereas the proposed architecture avoids long acquisition times due to sequential sensing. This one-shot procedure also allows to employ a fixed sensing matrix instead of a complex device such as a Digital Micro Mirror array or Spatial Light Modulator. It also enables imaging at bandwidths where these are not efficient.

Connected through hyperedge HE-1 (induced by source paper "multispectral compressive imaging strategies using fabry p erot filtered sensors"):
  with members:
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "self calibration and biconvex compressive sensing"
    - "generalized inpainting method for hyperspectral image acquisition"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-2 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "low complexity multiclass encryption by compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "through the haze a non convex approach to blind gain calibration for linear random sensing models"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind sensor calibration using approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "a non convex blind calibration method for randomised sensing strategies"):
  with members:
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "convex optimization approaches for blind sensor calibration using sparsity"

Connected through hyperedge HE-5 (induced by source paper "generalized inpainting method for hyperspectral image acquisition"):
  with members:
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "graded quantization for multiple description coding of compressive measurements"):
  with members:
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "graded quantization democracy for multiple descriptions in 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.CV(Computer Vision and Pattern Recognition)