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: "compressive sensing with redundant dictionaries and structured measurements"
  Abstract: Consider the problem of recovering an unknown signal from undersampled measurements, given the knowledge that the signal has a sparse representation in a specified dictionary $D$. This problem is now understood to be well-posed and efficiently solvable under suitable assumptions on the measurements and dictionary, if the number of measurements scales roughly with the sparsity level. One sufficient condition for such is the $D$-restricted isometry property ($D$-RIP), which asks that the sampling matrix approximately preserve the norm of all signals which are sufficiently sparse in $D$. While many classes of random matrices are known to satisfy such conditions, such matrices are not representative of the structural constraints imposed by practical sensing systems. We close this gap in the theory by demonstrating that one can subsample a fixed orthogonal matrix in such a way that the $D$-RIP will hold, provided this basis is sufficiently incoherent with the sparsifying dictionary $D$. We also extend this analysis to allow for weighted sparse expansions. Consequently, we arrive at compressive sensing recovery guarantees for structured measurements and redundant dictionaries, opening the door to a wide array of practical applications.

Connected through hyperedge HE-1 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "why are deep nets reversible a simple theory with implications for training"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"

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"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "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 "total variation minimization in compressed sensing"):
  with members:
    - "convex recovery of a structured signal from independent random linear 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"
    - "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-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"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "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-5 (induced by source paper "preserving injectivity under subgaussian mappings and its application to compressed sensing"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "the gap between the null space property and the restricted isometry property"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "one bit compressive sensing of dictionary sparse signals"):
  with members:
    - "quantization of compressive samples with stable and robust recovery"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sampling and reconstructing signals from a union of linear subspaces"

Connected through hyperedge HE-7 (induced by source paper "constrained adaptive sensing"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "nearly optimal sparse fourier transform"
    - "compressive binary search"
    - "stable and robust sampling strategies for compressive imaging"
    - "on the power of adaptivity in sparse recovery"
    - "how well can we estimate a sparse vector"

Connected through hyperedge HE-8 (induced by source paper "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"):
  with members:
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "recovering low rank matrices from few coefficients in any basis"
    - "on variable density compressive sampling"
    - "a probabilistic and ripless theory of compressed sensing"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

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)