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: "robust width a characterization of uniformly stable and robust compressed sensing"
  Abstract: Compressed sensing seeks to invert an underdetermined linear system by exploiting additional knowledge of the true solution. Over the last decade, several instances of compressed sensing have been studied for various applications, and for each instance, reconstruction guarantees are available provided the sensing operator satisfies certain sufficient conditions. In this paper, we completely characterize the sensing operators which allow uniformly stable and robust reconstruction by convex optimization for many of these instances. The characterized sensing operators satisfy a new property we call the robust width property, which simultaneously captures notions of widths from approximation theory and of restricted eigenvalues from statistical regression. We provide a geometric interpretation of this property, we discuss its relationship with the restricted isometry property, and we apply techniques from geometric functional analysis to find random matrices which satisfy the property with high probability.

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"
    - "on robust width property for lasso and dantzig selector"
    - "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 "preserving injectivity under subgaussian mappings and its application to compressed sensing"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "the gap between the null space property and the restricted isometry property"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-3 (induced by source paper "stable and robust ell_p constrained compressive sensing recovery via robust width property"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "restricted isometry property for general p norms"
    - "stable image reconstruction using total variation minimization"
    - "structured compressed sensing from theory to applications"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-4 (induced by source paper "discrete uncertainty principles and sparse signal processing"):
  with members:
    - "recovery of signals with low density"
    - "convexity in source separation models geometry and algorithms"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "estimating unknown sparsity in compressed sensing"
    - "sharp recovery bounds for convex demixing with applications"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "compressed sensing how sharp is the restricted isometry property"
    - "the sparsity gap uncertainty principles proportional to dimension"

Connected through hyperedge HE-5 (induced by source paper "the gap between the null space property and the restricted isometry property"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "on the gap between rip properties and sparse recovery conditions"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "phase transitions for greedy sparse approximation algorithms"

Connected through hyperedge HE-6 (induced by source paper "a remark on weaken restricted isometry property in compressed sensing"):
  with members:
    - "on robust width property for lasso and dantzig selector"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"

Connected through hyperedge HE-7 (induced by source paper "on robust width property for lasso and dantzig selector"):
  with members:
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"

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)