human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "structure dependent sampling in compressed sensing theoretical guarantees for tight frames":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing")
  Member "sampling in the analysis transform domain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "analysis ell_1 recovery with frames and gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse label propagation")
  Member "signal space cosamp for sparse recovery with redundant dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing")
  Member "stable image reconstruction using total variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "super resolution from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "restricted isometry of fourier matrices and list decodability of random linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")

Question: Please tell me which class the source paper 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)