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: "block length dependent thresholds in block sparse compressed sensing"
  Abstract: One of the most basic problems in compressed sensing is solving an under-determined system of linear equations. Although this problem seems rather hard certain l1-optimization algorithm appears to be very successful in solving it. The recent work of [14, 28] rigorously proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements of the unknown vector) also proportional to the length of the unknown vector such that l1-optimization algorithm succeeds in solving the system. In more recent papers [78,81] we considered the setup of the so-called block-sparse unknown vectors. In a large dimensional and statistical context, we determined sharp lower bounds on the values of allowable sparsity for any given number (proportional to the length of the unknown vector) of equations such that an l2/l1-optimization algorithm succeeds in solving the system. The results established in [78, 81] assumed a fairly large block-length of the block-sparse vectors. In this paper we consider the block-length to be a parameter of the system. Consequently, we then establish sharp lower bounds on the values of the allowable block-sparsity as functions of the block-length.

Connected through hyperedge HE-1 (induced by source paper "precise error analysis of regularized m estimators in high dimensions"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "the generalized lasso with non linear observations"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "convexity in source separation models geometry and algorithms"

Connected through hyperedge HE-2 (induced by source paper "compression based compressed sensing"):
  with members:
    - "universal compressed sensing"
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "sharp recovery bounds for convex demixing with applications"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"

Connected through hyperedge HE-3 (induced by source paper "uniform recovery of fusion frame structured sparse signals"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "group sparse model selection hardness and relaxations"
    - "recovering low rank matrices from few coefficients in any basis"
    - "sparse recovery from combined fusion frame measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "block sparsity coherence and efficient recovery"

Connected through hyperedge HE-4 (induced by source paper "sharp mse bounds for proximal denoising"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "a framework to characterize performance of lasso algorithms"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "guarantees of total variation minimization for signal recovery"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-5 (induced by source paper "the squared error of generalized lasso a precise analysis"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the achievable performance of convex demixing"
    - "living on the edge phase transitions in convex programs with random data"
    - "regularly random duality"
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "a framework to characterize performance of lasso algorithms"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "sharp mse bounds for proximal denoising"

Connected through hyperedge HE-6 (induced by source paper "compressed sensing of block sparse positive vectors"):
  with members:
    - "lifting l q optimization thresholds"
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "regularly random duality"
    - "lifting l 1 optimization strong and sectional thresholds"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-7 (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"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "optimality of ell_2 ell_1 optimization block length dependent thresholds"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "sparse recovery of positive signals with minimal expansion"
    - "simultaneously sparse solutions to linear inverse problems with multiple system matrices and a single observation vector"

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)