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: "improved bounds on restricted isometry constants for gaussian matrices"
  Abstract: The Restricted Isometry Constants (RIC) of a matrix $A$ measures how close to an isometry is the action of $A$ on vectors with few nonzero entries, measured in the $\ell^2$ norm. Specifically, the upper and lower RIC of a matrix $A$ of size $n\times N$ is the maximum and the minimum deviation from unity (one) of the largest and smallest, respectively, square of singular values of all ${N\choose k}$ matrices formed by taking $k$ columns from $A$. Calculation of the RIC is intractable for most matrices due to its combinatorial nature; however, many random matrices typically have bounded RIC in some range of problem sizes $(k,n,N)$. We provide the best known bound on the RIC for Gaussian matrices, which is also the smallest known bound on the RIC for any large rectangular matrix. Improvements over prior bounds are achieved by exploiting similarity of singular values for matrices which share a substantial number of columns.

Connected through hyperedge HE-1 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-2 (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"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "compressed sensing how sharp is 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"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-4 (induced by source paper "replica symmetric bound for restricted isometry constant"):
  with members:
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing with prior information optimal strategies geometry and bounds"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "off the grid spectral compressed sensing with prior information"
    - "sharp mse bounds for proximal denoising"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-6 (induced by source paper "restricted isometry random variables probability distributions ric prediction and phase transition analysis for gaussian encoders"):
  with members:
    - "optimal phase transitions in compressed sensing"
    - "phase transitions for greedy sparse approximation algorithms"
    - "restricted isometries for partial random circulant matrices"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "oracle order recovery performance of greedy pursuits with replacement against general perturbations"):
  with members:
    - "perturbation analysis of orthogonal matching pursuit"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressed sensing performance bounds under poisson noise"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-8 (induced by source paper "towards a better compressed sensing"):
  with members:
    - "lifting l q optimization thresholds"
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"
    - "meshes that trap random subspaces"
    - "compressed sensing how sharp is the restricted isometry property"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "message passing algorithms 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)