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: "on robust width property for lasso and dantzig selector"
  Abstract: Recently, Cahill and Mixon completely characterized the sensing operators in many compressed sensing instances with a robust width property. The proposed property allows uniformly stable and robust reconstruction of certain solutions from an underdetermined linear system via convex optimization. However, their theory does not cover the Lasso and Dantzig selector models, both of which are popular alternatives in the statistics community. In this letter, we show that the robust width property can be perfectly applied to these two models as well. Our results solve an open problem left by Cahill and Mixon.

Connected through hyperedge HE-1 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values"):
  with members:
    - "stable and robust ell_p constrained compressive sensing recovery via robust width property"
    - "unknown sparsity in compressed sensing denoising and inference"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "estimating unknown sparsity in compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "one bit compressed sensing by linear programming"
    - "performance analysis of sparse recovery based on constrained minimal singular values"

Connected through hyperedge HE-2 (induced by source paper "a remark on weaken restricted isometry property in compressed sensing"):
  with members:
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"

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)