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 "uniform recovery bounds for structured random matrices in corrupted compressed sensing"):
  Abstract: We study the problem of recovering an   $s$   -sparse signal   $\mathbf{x}^{\star }\in \mathbb {C}^n$    from corrupted measurements    $\mathbf{y}=\mathbf{A}\mathbf{x}^{\star }+\mathbf{z}^{\star }+\mathbf{w}$  , where   $\mathbf{z}^{\star }\in \mathbb {C}^m$   is a   $k$  -sparse corruption vector whose nonzero entries may be arbitrarily large and   $\mathbf{w}\in \mathbb {C}^m$    is a dense noise with bounded energy. The aim is to exactly and stably recover the sparse signal with tractable optimization programs. In this paper, we prove the  uniform recovery guarantee  of this problem for two classes of structured sensing matrices. The first class can be expressed as the product of a unit-norm tight frame (UTF), a random diagonal matrix, and a bounded columnwise orthonormal matrix (e.g., partial random circulant matrix). When the UTF is bounded (i.e.   $\mu (\mathbf{U})\sim 1/\sqrt{m}$  ), we prove that with high probability, one can recover an   $s$  -sparse signal exactly and stably by   $l_1$   minimization programs even if the measurements are corrupted by a sparse vector, provided    $m=\mathcal{O}(s\log ^2s\log ^2n)$   and the sparsity level    $k$   of the corruption is a constant fraction of the total number of measurements. The second class considers a randomly subsampled orthonormal matrix (e.g., random Fourier matrix). We prove the uniform recovery guarantee provided that the corruption is sparse on certain sparsifying domain. Numerous simulation results are also presented to verify and complement the theoretical results.

Includes members:
  Member "the restricted isometry property of subsampled fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "modulated unit norm tight frames for compressed sensing":
    also connected through other hyperedges:
      (none)
  Member "corrupted sensing novel guarantees for separating structured signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "convolutional compressed sensing using deterministic sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios")
  Member "the restricted isometry property for random block diagonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sampling of graph signals via randomized local aggregations")
  Member "probabilistic recovery guarantees for sparsely corrupted signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty relations and sparse signal recovery")
  Member "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property")
  Member "compressed sensing and matrix completion with constant proportion of corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")

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)