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 "nearly optimal bounds for orthogonal least squares"):
  Abstract: In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On the one hand, we show that if the sampling matrix $\mathbf{A}$ satisfies the restricted isometry property (RIP) of order $K + 1$ with isometry constant $$ \delta_{K + 1} < \frac{1}{\sqrt{K+1}}, $$ then OLS exactly recovers the support of any $K$-sparse vector $\mathbf{x}$ from its samples $\mathbf{y} = \mathbf{A} \mathbf{x}$ in $K$ iterations. On the other hand, we show that OLS may not be able to recover the support of a $K$-sparse vector $\mathbf{x}$ in $K$ iterations for some $K$ if $$ \delta_{K + 1} \geq \frac{1}{\sqrt{K+\frac{1}{4}}}. $$

Includes members:
  Member "on the exact recovery condition of simultaneous orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach")
  Member "a sharp restricted isometry constant bound of orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint sparse recovery using signal space matching pursuit")
  Member "sharp conditions for exact support recovery of sparse signals with noise via omp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit")
  Member "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when does omp achieve support recovery with continuous dictionaries")
  Member "perturbation analysis of orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "preconditioned multiple orthogonal least squares and applications in ghost imaging via sparsity constraint")
  Member "exact recovery conditions for sparse representations with partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an optimal restricted isometry condition for exact sparse recovery with orthogonal least squares")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random access for massive machine type communications")

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)