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: "orthogonal matching pursuit with replacement"
  Abstract: In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose a novel partial hard-thresholding operator that leads to a general family of iterative algorithms. While one extreme of the family yields well known hard thresholding algorithms like ITI (Iterative Thresholding with Inversion) and HTP (Hard Thresholding Pursuit), the other end of the spectrum leads to a novel algorithm that we call Orthogonal Matching Pursuit with Replacement (OMPR). OMPR, like the classic greedy algorithm OMP, adds exactly one coordinate to the support at each iteration, based on the correlation with the current residual. However, unlike OMP, OMPR also removes one coordinate from the support. This simple change allows us to prove that OMPR has the best known guarantees for sparse recovery in terms of the Restricted Isometry Property (a condition on the measurement matrix). In contrast, OMP is known to have very weak performance guarantees under RIP. Given its simple structure, we are able to extend OMPR using locality sensitive hashing to get OMPR-Hash, the first provably sub-linear (in dimensionality) algorithm for sparse recovery. Our proof techniques are novel and flexible enough to also permit the tightest known analysis of popular iterative algorithms such as CoSaMP and Subspace Pursuit. We provide experimental results on large problems providing recovery for vectors of size up to million dimensions. We demonstrate that for large-scale problems our proposed methods are more robust and faster than existing methods.

Connected through hyperedge HE-1 (induced by source paper "noisy subspace clustering via matching pursuits"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "greedy feature selection for subspace clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "sketching for large scale learning of mixture models"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "a la carte learning fast kernels"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-3 (induced by source paper "distributed compressive sensing performance analysis with diverse signal ensembles"):
  with members:
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "living on the edge phase transitions in convex programs with random data"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "sparse recovery from combined fusion frame measurements"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-4 (induced by source paper "newtonized orthogonal matching pursuit frequency estimation over the continuum"):
  with members:
    - "compressive parameter estimation in awgn"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-5 (induced by source paper "improving the correlation lower bound for simultaneous orthogonal matching pursuit"):
  with members:
    - "on the noise robustness of simultaneous orthogonal matching pursuit"
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "distributed compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "convex relaxations of structured matrix factorizations"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "convex approaches to model wavelet sparsity patterns"

Connected through hyperedge HE-7 (induced by source paper "on iterative hard thresholding methods for high dimensional m estimation"):
  with members:
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "on learning discrete graphical models using greedy methods"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "matching pursuit lasso part ii applications and sparse recovery over batch signals"):
  with members:
    - "lensless imaging by compressive sensing"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "structured compressed sensing from theory to applications"
    - "leveraging billions of faces to overcome performance barriers in unconstrained face recognition"
    - "parallel coordinate descent for l1 regularized loss minimization"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "new bounds for restricted isometry constants"

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)