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 recovery based on q ratio constrained minimal singular values"):
  Abstract: We study verifiable sufficient conditions and computable performance bounds for sparse recovery algorithms such as the Basis Pursuit, the Dantzig selector and the Lasso estimator, in terms of a newly defined family of quality measures for the measurement matrices. With high probability, the developed measures for subgaussian random matrices are bounded away from zero as long as the number of measurements is reasonably large. Comparing to the restricted isotropic constant based performance analysis, the arguments in this paper are much more concise and the obtained bounds are tighter. Numerical experiments are presented to illustrate our theoretical results.

Includes members:
  Member "stable and robust ell_p constrained compressive sensing recovery via robust width property":
    also connected through other hyperedges:
      (none)
  Member "unknown sparsity in compressed sensing denoising and inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on q ratio cmsv for sparse recovery")
  Member "on robust width property for lasso and dantzig selector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a remark on weaken restricted isometry property in compressed sensing")
  Member "robust width a characterization of uniformly stable and robust compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "preserving injectivity under subgaussian mappings and its application to compressed sensing")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "estimating unknown sparsity in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "sparse volterra and polynomial regression models recoverability and estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)