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: "guarantees of total variation minimization for signal recovery"
  Abstract: In this paper, we consider using total variation minimization to recover signals whose gradients have a sparse support, from a small number of measurements. We establish the proof for the performance guarantee of total variation (TV) minimization in recovering \emph{one-dimensional} signal with sparse gradient support. This partially answers the open problem of proving the fidelity of total variation minimization in such a setting \cite{TVMulti}. In particular, we have shown that the recoverable gradient sparsity can grow linearly with the signal dimension when TV minimization is used. Recoverable sparsity thresholds of TV minimization are explicitly computed for 1-dimensional signal by using the Grassmann angle framework. We also extend our results to TV minimization for multidimensional signals. Stability of recovering signal itself using 1-D TV minimization has also been established through a property called "almost Euclidean property for 1-dimensional TV norm". We further give a lower bound on the number of random Gaussian measurements for recovering 1-dimensional signal vectors with $N$ elements and $K$-sparse gradients. Interestingly, the number of needed measurements is lower bounded by $\Omega((NK)^{\frac{1}{2}})$, rather than the $O(K\log(N/K))$ bound frequently appearing in recovering $K$-sparse signal vectors.

Connected through hyperedge HE-1 (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-2 (induced by source paper "block sparse recovery of semidefinite systems and generalized null space conditions"):
  with members:
    - "sparse recovery with integrality constraints"
    - "compressed sensing of block sparse positive vectors"
    - "stable image reconstruction using total variation minimization"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-3 (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"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "learning without concentration"
    - "structured random measurements in signal processing"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "spread spectrum magnetic resonance imaging"

Connected through hyperedge HE-4 (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 analysis l1 recovery from gaussian measurements and total variation minimization"
    - "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"
    - "oblique pursuits for compressed sensing"
    - "stable image reconstruction using total variation minimization"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-5 (induced by source paper "precise phase transition of total variation minimization"):
  with members:
    - "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"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "robust analysis l1 recovery from gaussian measurements and total variation minimization"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "stable image reconstruction using total variation minimization"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-7 (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"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-8 (induced by source paper "simple bounds for noisy linear inverse problems with exact side information"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "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"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "estimation of simultaneously sparse and low rank matrices"
    - "compressive principal component pursuit"
    - "compressed sensing over ell_p balls minimax mean square error"

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)