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: "analysis ell_1 recovery with frames and gaussian measurements"
  Abstract: This paper provides novel results for the recovery of signals from undersampled measurements based on analysis $\ell_1$-minimization, when the analysis operator is given by a frame. We both provide so-called uniform and nonuniform recovery guarantees for cosparse (analysis-sparse) signals using Gaussian random measurement matrices. The nonuniform result relies on a recovery condition via tangent cones and the uniform recovery guarantee is based on an analysis version of the null space property. Examining these conditions for Gaussian random matrices leads to precise bounds on the number of measurements required for successful recovery. In the special case of standard sparsity, our result improves a bound due to Rudelson and Vershynin concerning the exact reconstruction of sparse signals from Gaussian measurements with respect to the constant and extends it to stability under passing to approximately sparse signals and to robustness under noise on the measurements.

Connected through hyperedge HE-1 (induced by source paper "sparse label propagation"):
  with members:
    - "semi supervised learning via sparse label propagation"
    - "discrete signal processing on graphs sampling theory"
    - "network lasso clustering and optimization in large graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-2 (induced by source paper "compressive sensing with redundant dictionaries and structured measurements"):
  with members:
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "super resolution from noisy data"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "towards a mathematical theory of super resolution"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"

Connected through hyperedge HE-3 (induced by source paper "robust analysis l1 recovery from gaussian measurements and total variation minimization"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "guarantees of total variation minimization for signal recovery"
    - "stable image reconstruction using total variation minimization"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sampling in the analysis transform domain"):
  with members:
    - "analysis operator learning and its application to image reconstruction"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "stable and robust sampling strategies for compressive imaging"
    - "optimization with sparsity inducing penalties"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-5 (induced by source paper "on the effective measure of dimension in the analysis cosparse model"):
  with members:
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "matrix completion with noise"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "spread spectrum magnetic resonance imaging"
    - "theory and applications of compressed sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-7 (induced by source paper "guarantees of total variation minimization for signal recovery"):
  with members:
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "living on the edge phase transitions in convex programs with random data"
    - "stable recovery with analysis decomposable priors"
    - "sharp mse bounds for proximal denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "stable image reconstruction using total variation minimization"
    - "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-8 (induced by source paper "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"):
  with members:
    - "necessary and sufficient conditions of solution uniqueness in 1 norm minimization"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "stable recovery with analysis decomposable priors"
    - "stable image reconstruction using total variation minimization"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "stable and robust sampling strategies for compressive imaging"
    - "robust sparse analysis regularization"
    - "a probabilistic and ripless theory of 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)