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: "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
  Abstract: Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of (Adcock, Hansen, Poon and Roman, arXiv:1302.0561, 2013) to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities -- the sparsity in each level under the sparsifying operator.

Connected through hyperedge HE-1 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "spread spectrum compressed sensing mri using chirp radio frequency pulses"

Connected through hyperedge HE-2 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde"):
  with members:
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "analyzing the structure of multidimensional compressed sensing problems through coherence"
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "sampling in the analysis transform domain"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "ripless compressed sensing from anisotropic measurements"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "non uniform recovery guarantees for binary measurements and infinite dimensional compressed sensing"):
  with members:
    - "linear reconstructions and the analysis of the stable sampling rate"
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-4 (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"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "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-5 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"):
  with members:
    - "uniform recovery from subgaussian multi sensor measurements"
    - "compressed sensing and parallel acquisition"
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "refined analysis of sparse mimo radar"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"

Connected through hyperedge HE-6 (induced by source paper "analyzing the structure of multidimensional compressed sensing problems through coherence"):
  with members:
    - "compressed sensing with structured sparsity and structured acquisition"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "spread spectrum magnetic resonance imaging"
    - "stable and robust sampling strategies for compressive imaging"
    - "on variable density compressive sampling"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "compressive sensing with redundant dictionaries and structured measurements"):
  with members:
    - "sampling in the analysis transform domain"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "super resolution from noisy data"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "towards a mathematical theory of super resolution"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"

Connected through hyperedge HE-8 (induced by source paper "breaking the coherence barrier a new theory for compressed sensing"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "lensless imaging by compressive sensing"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "an analysis of block sampling strategies in compressed sensing"
    - "communications inspired projection design with application to compressive 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)