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 "compressive hyperspectral imaging with side information"):
  Abstract: A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary in situ from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information.

Includes members:
  Member "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information")
  Member "task driven adaptive statistical compressive sensing of gaussian mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information")
  Member "communications inspired projection design with application to compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "measurement matrix design for phase retrieval based on mutual information")
  Member "statistical compressed sensing of gaussian mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed anomaly detection with multiple mixed observations")
  Member "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neumann networks for inverse problems in imaging")
  Member "blind compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an algebraic geometric approach for linear regression without correspondences")

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.CV(Computer Vision and Pattern Recognition)