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: "simple bounds for recovering low complexity models"
  Abstract: This note presents a unified analysis of the recovery of simple objects from random linear measurements. When the linear functionals are Gaussian, we show that an s-sparse vector in R^n can be efficiently recovered from 2s log n measurements with high probability and a rank r, n by n matrix can be efficiently recovered from r(6n-5r) with high probability. For sparse vectors, this is within an additive factor of the best known nonasymptotic bounds. For low-rank matrices, this matches the best known bounds. We present a parallel analysis for block sparse vectors obtaining similarly tight bounds. In the case of sparse and block sparse signals, we additionally demonstrate that our bounds are only slightly weakened when the measurement map is a random sign matrix. Our results are based on analyzing a particular dual point which certifies optimality conditions of the respective convex programming problem. Our calculations rely only on standard large deviation inequalities and our analysis is self-contained.

Connected through hyperedge HE-1 (induced by source paper "on the error in phase transition computations for compressed sensing"):
  with members:
    - "exploiting prior information in block sparse signals"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp mse bounds for proximal denoising"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "sample complexity of total variation minimization"):
  with members:
    - "total variation minimization in compressed sensing"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-3 (induced by source paper "a fast implementation of singular value thresholding algorithm using recycling rank revealing randomized singular value decomposition"):
  with members:
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "sharp time data tradeoffs for linear inverse problems"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "the generalized lasso with non linear observations"
    - "does ell_p minimization outperform ell_1 minimization"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "learning without concentration"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "from denoising to compressed sensing"
    - "simple bounds for noisy linear inverse problems with exact side information"

Connected through hyperedge HE-5 (induced by source paper "blind deconvolution meets blind demixing algorithms and performance bounds"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "self calibration and biconvex compressive sensing"
    - "sparse signal processing concepts for efficient 5g system design"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "convexity in source separation models geometry and algorithms"

Connected through hyperedge HE-6 (induced by source paper "necessary and sufficient conditions of solution uniqueness in 1 norm minimization"):
  with members:
    - "constructing test instances for basis pursuit denoising"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "statistical estimation from denoising to sparse regression and hidden cliques"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "from denoising to compressed sensing"
    - "replica analysis and approximate message passing decoder for superposition codes"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-8 (induced by source paper "corrupted sensing novel guarantees for separating structured signals"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "signal recovery on incoherent manifolds"
    - "sharp recovery bounds for convex demixing with applications"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

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)