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: "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.

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)