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: "convex recovery of a structured signal from independent random linear measurements"
  Abstract: This chapter develops a theoretical analysis of the convex programming method for recovering a structured signal from independent random linear measurements. This technique delivers bounds for the sampling complexity that are similar to recent results for standard Gaussian measurements, but the argument applies to a much wider class of measurement ensembles. To demonstrate the power of this approach, the chapter presents a short analysis of phase retrieval by trace-norm minimization. The key technical tool is a framework, due to Mendelson and coauthors, for bounding a nonnegative empirical process.

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"
    - "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"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-2 (induced by source paper "complex phase retrieval from subgaussian measurements"):
  with members:
    - "on the convex geometry of blind deconvolution and matrix completion"
    - "phase retrieval without small ball probability assumptions"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "stable low rank matrix recovery via null space properties"

Connected through hyperedge HE-3 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "sparse signal processing concepts for efficient 5g system design"

Connected through hyperedge HE-4 (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"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on the effective measure of dimension in the analysis cosparse model"

Connected through hyperedge HE-5 (induced by source paper "simultaneous phase retrieval and blind deconvolution via convex programming"):
  with members:
    - "blind deconvolutional phase retrieval via convex programming"
    - "anchored regression solving random convex equations via convex programming"
    - "benchmark problems for phase retrieval"
    - "phasemax convex phase retrieval via basis pursuit"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "phase retrieval from coded diffraction patterns"
    - "blind deconvolution using convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-6 (induced by source paper "exploiting prior information in block sparse signals"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "performance limits for noisy multimeasurement vector problems"
    - "spectral super resolution with prior knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "recovering compressively sampled signals using partial support information"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-7 (induced by source paper "recovery of structured signals from corrupted non linear measurements"):
  with members:
    - "corrupted sensing with sub gaussian measurements"
    - "stable recovery of structured signals from corrupted sub gaussian measurements"
    - "on the phase transition of corrupted sensing"
    - "the generalized lasso with non linear observations"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp recovery bounds for convex demixing with applications"

Connected through hyperedge HE-8 (induced by source paper "sparse non negative recovery from biased subgaussian measurements using nnls"):
  with members:
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "visible light communications based indoor positioning via compressed sensing"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "learning without concentration"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "compressed sensing performance bounds under poisson noise"

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)