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: "various thresholds for ell_1 optimization in compressed sensing"
  Abstract: Recently, \cite{CRT,DonohoPol} theoretically analyzed the success of a polynomial $\ell_1$-optimization algorithm in solving an under-determined system of linear equations. In a large dimensional and statistical context \cite{CRT,DonohoPol} proved 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 $\ell_1$-optimization succeeds in solving the system. In this paper, we provide an alternative performance analysis of $\ell_1$-optimization and obtain the proportionality constants that in certain cases match or improve on the best currently known ones from \cite{DonohoPol,DT}.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery"):
  with members:
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "the generalized lasso with non linear observations"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "sharp mse bounds for proximal denoising"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-2 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization"):
  with members:
    - "on the error in phase transition computations for compressed sensing"
    - "sample complexity of total variation minimization"
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "learning compact neural networks with regularization"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "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"

Connected through hyperedge HE-4 (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"
    - "simple bounds for recovering low complexity models"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-5 (induced by source paper "persistent homology for low complexity models"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "practical and optimal lsh for angular distance"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "local doubling dimension of point sets"
    - "a unified framework for linear dimensionality reduction in l1"

Connected through hyperedge HE-6 (induced by source paper "how to exploit prior information in low complexity models"):
  with members:
    - "collaborative total variation a general framework for vectorial tv models"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "a short note on compressed sensing with partially known signal support"

Connected through hyperedge HE-7 (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-8 (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"

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)