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: "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
  Abstract: This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrix recovery. The analysis relies on a key technical tool which represents points in a polytope by convex combinations of sparse vectors. The technique is elementary while leads to sharp results. #R##N#It is shown that for any given constant $t\ge {4/3}$, in compressed sensing $\delta_{tk}^A  0$, $\delta_{tk}^A<\sqrt{\frac{t-1}{t}}+\epsilon$ is not sufficient to guarantee the exact recovery of all $k$-sparse signals for large $k$. Similar result also holds for matrix recovery. In addition, the conditions $\delta_{tk}^A < \sqrt{(t-1)/t}$ and $\delta_{tr}^\mathcal{M}< \sqrt{(t-1)/t}$ are also shown to be sufficient respectively for stable recovery of approximately sparse signals and low-rank matrices in the noisy case.

Connected through hyperedge HE-1 (induced by source paper "the importance of phase in complex compressive sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "uniform recovery of fusion frame structured sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "an optimal condition of robust low rank matrices recovery"):
  with members:
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "nearly optimal bounds for orthogonal least squares"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "success probability of the babai estimators for box constrained integer linear models"
    - "sharp rip bound for sparse signal and low rank matrix recovery"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "provable self representation based outlier detection in a union of subspaces"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse modeling for image and vision processing"
    - "greedy feature selection for subspace clustering"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

Connected through hyperedge HE-6 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information"):
  with members:
    - "signal recovery under cumulative coherence"
    - "recovery of signals under the condition on ric and roc via prior support information"
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-7 (induced by source paper "rip based performance guarantee for low tubal rank tensor recovery"):
  with members:
    - "low rank tensor recovery via iterative hard thresholding"
    - "compressive sensing of sparse tensors"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"

Connected through hyperedge HE-8 (induced by source paper "on the restricted isometry property of centered self khatri rao products"):
  with members:
    - "massive mimo unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "multiple measurement vectors problem a decoupling property and its applications"

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)