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 "sparse signal recovery using gomp assisted mols"):
  Abstract: Because of fast convergence in finite number of steps and low computational complexity, signal recovery from compressed measurements using greedy algorithms have generated a large amount of interest in recent years. Among these greedy algorithms OMP is well studied and recently its generalization, gOMP, have also drawn attention. On the other hand OLS and its generalization mOLS have been studied in recent years because of their potential advantages in signal recovery guarantee compared to OMP or gOMP. But OLS and mOLS have the shortcomings of high computational complexity. In this paper we propose a new algorithm which uses gOMP to preselect a N length set out of which mOLS selects its L best coefficients. We have shown that our new algorithm, named gOMPamOLS, is guaranteed to reconstruct a K sparse signals perfectly from compressed measurements if the restricted isometry constant satisfies $\delta_{LK+N}<\frac{\sqrt{L}}{\sqrt{L}+\sqrt{K+L}}$ . Moreover experimental results indicate that gOMPamOLS is robust under sensing matrix with correlated dictionary in terms of signal reconstruction and is much faster than OLS.

Includes members:
  Member "a novel sufficient condition for generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      (none)
  Member "improving the bound on the rip constant in generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a sharp bound on ric in generalized orthogonal matching pursuit")
  Member "generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random access for massive machine type communications")
  Member "orthogonal matching pursuit a brownian motion analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the noise robustness of simultaneous orthogonal matching pursuit")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)