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 "orthogonal matching pursuit with thresholding and its application in compressive sensing"):
  Abstract: Greed is good. However, the tighter you squeeze, the less you have. In this paper, a less greedy algorithm for sparse signal reconstruction in compressive sensing, named orthogonal matching pursuit with thresholding is studied. Using the global 2-coherence , which provides a "bridge" between the well known mutual coherence and the restricted isometry constant, the performance of orthogonal matching pursuit with thresholding is analyzed and more general results for sparse signal reconstruction are obtained. It is also shown that given the same assumption on the coherence index and the restricted isometry constant as required for orthogonal matching pursuit, the thresholding variation gives exactly the same reconstruction performance with significantly less complexity.

Includes members:
  Member "new coherence and rip analysis for weak orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a sharp restricted isometry constant bound of orthogonal matching pursuit")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "structured compressed sensing from theory to applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "serial quantization for sparse time sequences")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "average case analysis of multichannel sparse recovery using convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "new bounds for restricted isometry constants":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the use of mutual coherence to prove ell 1 ell 0 equivalence in classification problems")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)