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 imaging by compressive sensing"
  Abstract: In this paper, we propose a lensless compressive imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element. A compressive sensing matrix is implemented by adjusting the transmittance of the individual aperture elements according to the values of the sensing matrix. The proposed architecture is simple and reliable because no lens is used. The architecture can be used for capturing images of visible and other spectra such as infrared, or millimeter waves, in surveillance applications for detecting anomalies or extracting features such as speed of moving objects. Multiple sensors may be used with a single aperture assembly to capture multiview images simultaneously. A prototype was built by using a LCD panel and a photoelectric sensor for capturing images of visible spectrum.

Connected through hyperedge HE-1 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"):
  with members:
    - "linear reconstructions and the analysis of the stable sampling rate"
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"
    - "compressive single pixel fourier transform imaging using structured illumination"
    - "compressive hyperspectral imaging fourier transform interferometry meets single pixel camera"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "compressive imaging using fast transform coding"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"

Connected through hyperedge HE-2 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"

Connected through hyperedge HE-3 (induced by source paper "non uniform recovery guarantees for binary measurements and infinite dimensional compressed sensing"):
  with members:
    - "linear reconstructions and the analysis of the stable sampling rate"
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "snapshot compressed sensing performance bounds and algorithms"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "an efficient algorithm for compression based compressed sensing"
    - "deep fully connected networks for video compressive sensing"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "compressive hyperspectral imaging with side information"

Connected through hyperedge HE-5 (induced by source paper "binary sparse bayesian learning algorithm for one bit compressed sensing"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "block wise lensless compressive camera"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "block wise lensless compressive camera"):
  with members:
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"
    - "flatcam thin bare sensor cameras using coded aperture and computation"
    - "coded aperture compressive temporal imaging"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-8 (induced by source paper "toward depth estimation using mask based lensless cameras"):
  with members:
    - "diffusercam lensless single exposure 3d imaging"
    - "flatcam thin bare sensor cameras using coded aperture and computation"

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)