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: "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
  Abstract: In compressed sensing, it is often desirable to consider signals possessing additional structure beyond sparsity. One such structured signal model - which forms the focus of this paper - is the local sparsity in levels class. This class has recently found applications in problems such as compressive imaging, multi-sensor acquisition systems and sparse regularization in inverse problems. In this paper we present uniform recovery guarantees for this class when the measurement matrix corresponds to a subsampled isometry. We do this by establishing a variant of the standard restricted isometry property for sparse in levels vectors, known as the restricted isometry property in levels. Interestingly, besides the usual log factors, our uniform recovery guarantees are simpler and less stringent than existing nonuniform recovery guarantees. For the particular case of discrete Fourier sampling with Haar wavelet sparsity, a corollary of our main theorem yields a new recovery guarantee which improves over the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography"
    - "adversarial attacks and defences a survey"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "optimal approximation of continuous functions by very deep relu networks"

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 structured sparsity and structured acquisition"

Connected through hyperedge HE-3 (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 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"
    - "spread spectrum compressed sensing mri using chirp radio frequency pulses"

Connected through hyperedge HE-4 (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"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"):
  with members:
    - "linear reconstructions and the analysis of the stable sampling rate"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "on oracle type local recovery guarantees in compressed sensing"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "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"
    - "stable and robust sampling strategies for compressive imaging"
    - "on variable density compressive sampling"

Connected through hyperedge HE-7 (induced by source paper "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness"):
  with members:
    - "compressed sensing with structured sparsity and structured acquisition"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "optimization with sparsity inducing penalties"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-8 (induced by source paper "uniform recovery from subgaussian multi sensor measurements"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "local sparsity and recovery of fusion frames structured signals"
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "multi view in lensless compressive imaging"
    - "the restricted isometry property for random block diagonal matrices"

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)