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 signal reconstruction via iterative support detection"
  Abstract: We present a novel sparse signal reconstruction method, iterative support detection (ISD), aiming to achieve fast reconstruction and a reduced requirement on the number of measurements compared to the classical $\ell_1$ minimization approach. ISD addresses failed reconstructions of $\ell_1$ minimization due to insufficient measurements. It estimates a support set $I$ from a current reconstruction and obtains a new reconstruction by solving the minimization problem $\min\{\sum_{i\notin I}|x_i|:Ax=b\}$, and it iterates these two steps for a small number of times. ISD differs from the orthogonal matching pursuit method, as well as its variants, because (i) the index set $I$ in ISD is not necessarily nested or increasing, and (ii) the minimization problem above updates all the components of $x$ at the same time. We generalize the null space property to the truncated null space property and present our analysis of ISD based on the latter. We introduce an efficient implementation of ISD, called threshold-ISD, for recovering signals with fast decaying distributions of nonzeros from compressive sensing measurements. Numerical experiments show that threshold-ISD has significant advantages over the classical $\ell_1$ minimization approach, as well as two state-of-the-art algorithms: the iterative reweighted $\ell_1$ minimization algorithm (IRL1) and the iterative reweighted least-squares algorithm (IRLS). MATLAB code is available for download from http://www.caam.rice.edu/ optimization/L1/ISD/.

Connected through hyperedge HE-1 (induced by source paper "truncated sparse approximation property and truncated q norm minimization"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressive multiplexing of correlated signals"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "on collaborative compressive sensing systems the framework design and algorithm"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "performance limits for noisy multimeasurement vector problems"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "sparsity averaging for compressive imaging"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "distributed compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "improving a omp"):
  with members:
    - "distributed greedy pursuit algorithms"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "map support detection for greedy sparse signal recovery algorithms in compressive sensing"):
  with members:
    - "bayesian pursuit algorithms"
    - "on finite alphabet compressive sensing"
    - "generalized orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "spark level sparsity and the ell_1 tail minimization"):
  with members:
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "support driven wavelet frame based image deblurring"):
  with members:
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "group based sparse representation for image restoration"
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"
    - "sparsity based methods for overparameterized variational problems"

Connected through hyperedge HE-8 (induced by source paper "iterative ell_1 minimization for non convex compressed sensing"):
  with members:
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "iterative hard thresholding for compressed sensing"

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)