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 how sharp is the restricted isometry property"
  Abstract: Compressed Sensing (CS) seeks to recover an unknown vector with $N$ entries by making far fewer than $N$ measurements; it posits that the number of compressed sensing measurements should be comparable to the information content of the vector, not simply $N$. CS combines the important task of compression directly with the measurement task. Since its introduction in 2004 there have been hundreds of manuscripts on CS, a large fraction of which develop algorithms to recover a signal from its compressed measurements. Because of the paradoxical nature of CS -- exact reconstruction from seemingly undersampled measurements -- it is crucial for acceptance of an algorithm that rigorous analyses verify the degree of undersampling the algorithm permits. The Restricted Isometry Property (RIP) has become the dominant tool used for the analysis in such cases. We present here an asymmetric form of RIP which gives tighter bounds than the usual symmetric one. We give the best known bounds on the RIP constants for matrices from the Gaussian ensemble. Our derivations illustrate the way in which the combinatorial nature of CS is controlled. Our quantitative bounds on the RIP allow precise statements as to how aggressively a signal can be undersampled, the essential question for practitioners. We also document the extent to which RIP gives precise information about the true performance limits of CS, by comparing with approaches from high-dimensional geometry.

Connected through hyperedge HE-1 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-2 (induced by source paper "approximately certifying the restricted isometry property is hard"):
  with members:
    - "average case hardness of rip certification"
    - "the restricted isometry property of subsampled fourier matrices"
    - "computational complexity of certifying restricted isometry property"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "hidden cliques and the certification of the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "on the construction of sparse matrices from expander graphs"):
  with members:
    - "on model based rip 1 matrices"
    - "vanishingly sparse matrices and expander graphs with application to compressed sensing"
    - "improved bounds on restricted isometry constants for gaussian matrices"

Connected through hyperedge HE-4 (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"
    - "model based compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "quantitative recovery conditions for tree based compressed sensing"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "approximation algorithms for model based compressive sensing"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "model based compressive sensing"
    - "phase transitions for greedy sparse approximation algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "discrete uncertainty principles and sparse signal processing"):
  with members:
    - "recovery of signals with low density"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "convexity in source separation models geometry and algorithms"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "estimating unknown sparsity in compressed sensing"
    - "sharp recovery bounds for convex demixing with applications"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "the sparsity gap uncertainty principles proportional to dimension"

Connected through hyperedge HE-7 (induced by source paper "robustness of sparse recovery via f minimization a topological viewpoint"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "approximate projected generalized gradient methods with sparsity inducing penalties"
    - "the convergence guarantees of a non convex approach for sparse recovery"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "optimal phase transitions in compressed sensing"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-8 (induced by source paper "replica symmetric bound for restricted isometry constant"):
  with members:
    - "improved bounds on restricted isometry constants for gaussian 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)