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: "sub nyquist sampling of sparse and correlated signals in array processing"
  Abstract: This paper considers efficient sampling of simultaneously sparse and correlated (S$\&$C) signals. Such signals arise in various applications in array processing. We propose an implementable sampling architecture for the acquisition of S$\&$C at a sub-Nyquist rate. We prove a sampling theorem showing exact and stable reconstruction of the acquired signals even when the sampling rate is smaller than the Nyquist rate by orders of magnitude. Quantitatively, our results state that an ensemble $M$ signals, composed of a-priori unknown latent $R$ signals, each bandlimited to $W/2$ but only $S$-sparse in the Fourier domain, can be reconstructed exactly from compressive sampling only at a rate $RS\log^{\alpha} W$ samples per second. When $R \ll M$, and $S\ll W$, this amounts to a significant reduction in sampling rate compared to the Nyquist rate of $MW$ samples per second. This is the first result that presents an implementable sampling architecture, and a sampling theorem for the compressive acquisition of S$\&$C signals. #R##N#The signal reconstruction from sub-Nyquist rate boils down to a sparse and low-rank (S$\&$L) matrix recovery from a few linear measurements. The conventional convex penalties for S$\&$L matrices are provably not optimal in the number of measurements. We resort to a two-step algorithm to recover S$\&$L matrix from a near-optimal number of measurements. This result then translates into a signal reconstruction algorithm from a sub-Nyquist sampling rate.

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)