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: "sampling and reconstructing signals from a union of linear subspaces"
  Abstract: In this note we study the problem of sampling and reconstructing signals which are assumed to lie on or close to one of several subspaces of a Hilbert space. Importantly, we here consider a very general setting in which we allow infinitely many subspaces in infinite dimensional Hilbert spaces. This general approach allows us to unify many results derived recently in areas such as compressed sensing, affine rank minimisation and analog compressed sensing. #R##N#Our main contribution is to show that a conceptually simple iterative projection algorithms is able to recover signals from a union of subspaces whenever the sampling operator satisfies a bi-Lipschitz embedding condition. Importantly, this result holds for all Hilbert spaces and unions of subspaces, as long as the sampling procedure satisfies the condition for the set of subspaces considered. In addition to recent results for finite unions of finite dimensional subspaces and infinite unions of subspaces in finite dimensional spaces, we also show that this bi-Lipschitz property can hold in an analog compressed sensing setting in which we have an infinite union of infinite dimensional subspaces living in infinite dimensional space.

Connected through hyperedge HE-1 (induced by source paper "neumann networks for inverse problems in imaging"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "off the grid model based deep learning o modl"
    - "multi shot sensitivity encoded diffusion mri using model based deep learning modl mussels"
    - "neural ordinary differential equations"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-2 (induced by source paper "back projection based fidelity term for ill posed linear inverse problems"):
  with members:
    - "super resolution via image adapted denoising cnns incorporating external and internal learning"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "image restoration by iterative denoising and backward projections"
    - "learning deep cnn denoiser prior for image restoration"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-3 (induced by source paper "quantization in compressive sensing a signal processing approach"):
  with members:
    - "methods for quantized compressed sensing"
    - "quantization and compressive sensing"
    - "noise folding in compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "sketching for large scale learning of mixture models"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "compressive k means"
    - "adapting to unknown noise level in sparse deconvolution"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"

Connected through hyperedge HE-5 (induced by source paper "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"):
  with members:
    - "better than real complex valued neural nets for mri fingerprinting"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "mr fingerprinting deep reconstruction network drone"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "a compressed sensing framework for magnetic resonance fingerprinting"
    - "sparse subspace clustering algorithm theory and applications"
    - "model based compressive sensing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "inexact gradient projection and fast data driven compressed sensing"):
  with members:
    - "sharp time data tradeoffs for linear inverse problems"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "matrix recipes for hard thresholding methods"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "noise folding in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-7 (induced by source paper "an efficient algorithm for compression based compressed sensing"):
  with members:
    - "compression based compressed sensing"
    - "new approach to bayesian high dimensional linear regression"
    - "from denoising to compressed sensing"
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "model based compressive sensing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

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.IT(Information Theory)