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: "stable and robust sampling strategies for compressive imaging"
  Abstract: In many signal processing applications, one wishes to acquire images that are sparse in transform domains such as spatial finite differences or wavelets using frequency domain samples. For such applications, overwhelming empirical evidence suggests that superior image reconstruction can be obtained through variable density sampling strategies that concentrate on lower frequencies. The wavelet and Fourier transform domains are not incoherent because low-order wavelets and low-order frequencies are correlated, so compressive sensing theory does not immediately imply sampling strategies and reconstruction guarantees. In this paper we turn to a more refined notion of coherence -- the so-called local coherence -- measuring for each sensing vector separately how correlated it is to the sparsity basis. For Fourier measurements and Haar wavelet sparsity, the local coherence can be controlled and bounded explicitly, so for matrices comprised of frequencies sampled from a suitable inverse square power-law density, we can prove the restricted isometry property with near-optimal embedding dimensions. Consequently, the variable-density sampling strategy we provide allows for image reconstructions that are stable to sparsity defects and robust to measurement noise. Our results cover both reconstruction by $\ell_1$-minimization and by total variation minimization. The local coherence framework developed in this paper should be of independent interest in sparse recovery problems more generally, as it implies that for optimal sparse recovery results, it suffices to have bounded \emph{average} coherence from sensing basis to sparsity basis -- as opposed to bounded maximal coherence -- as long as the sampling strategy is adapted accordingly.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery"):
  with members:
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "the generalized lasso with non linear observations"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "sharp mse bounds for proximal denoising"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde"):
  with members:
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "analyzing the structure of multidimensional compressed sensing problems through coherence"
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "ripless compressed sensing from anisotropic measurements"

Connected through hyperedge HE-4 (induced by source paper "self supervised deep active accelerated mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "unified theory for recovery of sparse signals in a general transform domain"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "on oracle type local recovery guarantees in compressed sensing"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "living on the edge phase transitions in convex programs with random data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "on variable density compressive sampling"

Connected through hyperedge HE-6 (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"
    - "a variable density sampling scheme for compressive fourier transform interferometry"
    - "compressive sampling approach for image acquisition with lensless endoscope"
    - "compressive imaging and characterization of sparse light deflection maps"

Connected through hyperedge HE-7 (induced by source paper "multilevel illumination coding for fourier transform interferometry in fluorescence spectroscopy"):
  with members:
    - "a variable density sampling scheme for compressive fourier transform interferometry"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "blind demixing and deconvolution at near optimal rate"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovering structured data from superimposed non linear measurements"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "low rank tensor recovery via iterative hard thresholding"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "rip like properties in subsampled blind deconvolution"

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)