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: "an optimal restricted isometry condition for exact sparse recovery with orthogonal least squares"
  Abstract: The orthogonal least squares (OLS) algorithm is popularly used in sparse recovery, subset selection, and function approximation. In this paper, we analyze the performance guarantee of OLS. Specifically, we show that if a sampling matrix $\mathbf{\Phi}$ has unit $\ell_{2}$-norm columns and satisfies the restricted isometry property (RIP) of order $K+1$ with \begin{align*} \delta_{K+1} &<C_{K} = \begin{cases} \frac{1}{\sqrt{K}}, & K=1, \frac{1}{\sqrt{K+\frac{1}{4}}}, & K=2, \frac{1}{\sqrt{K+\frac{1}{16}}}, & K=3, \frac{1}{\sqrt{K}}, & K \ge 4, \end{cases} \end{align*} then OLS exactly recovers any $K$-sparse vector $\mathbf{x}$ from its measurements $\mathbf{y} = \mathbf{\Phi} \mathbf{x}$ in $K$ iterations. Furthermore, we show that the proposed guarantee is optimal in the sense that OLS may fail the recovery under $\delta_{K+1} \ge C_{K}$. Additionally, we show that if the columns of a sampling matrix are $\ell_{2}$-normalized, then the proposed condition is also an optimal recovery guarantee for the orthogonal matching pursuit (OMP) algorithm. Also, we establish a recovery guarantee of OLS in the more general case where a sampling matrix might not have unit $\ell_{2}$-norm columns. Moreover, we analyze the performance of OLS in the noisy case. Our result demonstrates that under a suitable constraint on the minimum magnitude of nonzero elements in an input signal, the proposed RIP condition ensures OLS to identify the support exactly.

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)