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: "lensless compressive imaging"
  Abstract: We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens. An anytime algorithm is proposed to reconstruct images from the compressive measurements; the algorithm produces a sequence of solutions that monotonically converge to the true signal (thus, anytime). The algorithm is developed based on the sparsity of local overlapping patches (in the transformation domain) and state-of-the-art results have been obtained. Experiments on real data demonstrate that encouraging results are obtained by measuring about 10% (of the image pixels) compressive measurements. The reconstruction results of the proposed algorithm are compared with the JPEG compression (based on file sizes) and the reconstructed image quality is close to the JPEG compression, in particular at a high compression rate.

Connected through hyperedge HE-1 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "block wise lensless compressive camera"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "compressive hyperspectral imaging with side information"

Connected through hyperedge HE-2 (induced by source paper "block wise lensless compressive camera"):
  with members:
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "flatcam thin bare sensor cameras using coded aperture and computation"
    - "lensless imaging by compressive sensing"
    - "coded aperture compressive temporal imaging"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-3 (induced by source paper "generalized alternating projection based total variation minimization for compressive sensing"):
  with members:
    - "compressive hyperspectral imaging with side information"
    - "convergence of the generalized alternating projection algorithm for compressive sensing"
    - "tree structure bayesian compressive sensing for video"
    - "lensless imaging by compressive sensing"
    - "coded aperture compressive temporal imaging"
    - "multi view in lensless compressive imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "convergence of the generalized alternating projection algorithm for compressive sensing"):
  with members:
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"

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)