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: "robust analysis l1 recovery from gaussian measurements and total variation minimization"
  Abstract: Analysis l 1 -recovery refers to a technique of recovering a signal that is sparse in some transform domain from incomplete corrupted measurements. This includes total variation minimization as an important special case when the transform domain is generated by a difference operator. In the present paper, we provide a bound on the number of Gaussian measurements required for successful recovery for total variation and for the case that the analysis operator is a frame. The bounds are particularly suitable when the sparsity of the analysis representation of the signal is not very small.

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"
    - "sharp mse bounds for proximal denoising"
    - "stable image reconstruction using total variation minimization"
    - "stable and robust sampling strategies for compressive imaging"

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"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on the effective measure of dimension in the analysis cosparse model"

Connected through hyperedge HE-3 (induced by source paper "sample complexity of total variation minimization"):
  with members:
    - "total variation minimization in compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "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-4 (induced by source paper "total variation minimization in compressed sensing"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "learning without concentration"
    - "structured random measurements in signal processing"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "guarantees of total variation minimization for signal recovery"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "spread spectrum magnetic resonance imaging"

Connected through hyperedge HE-5 (induced by source paper "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"):
  with members:
    - "learning co sparse analysis operators with separable structures"
    - "precise phase transition of total variation minimization"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "on the effective measure of dimension in the analysis cosparse model"
    - "projection onto the cosparse set is np hard"
    - "analysis operator learning and its application to image reconstruction"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-6 (induced by source paper "unified theory for recovery of sparse signals in a general transform domain"):
  with members:
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "ripless compressed sensing from anisotropic measurements"
    - "living on the edge phase transitions in convex programs with random data"
    - "guarantees of total variation minimization for signal recovery"
    - "oblique pursuits for compressed sensing"
    - "stable image reconstruction using total variation minimization"
    - "stable and robust sampling strategies for compressive imaging"

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

Connected through hyperedge HE-8 (induced by source paper "compressed sensing with side information geometrical interpretation and performance bounds"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "sharp mse bounds for proximal denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "various thresholds for ell_1 optimization in 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)