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: "sparse approximation property and stable recovery of sparse signals from noisy measurements"
  Abstract: In this correspondence, we introduce a sparse approximation property of order s for a measurement matrix A:||xs||2 ≤ D||Ax||2+β(σs(x))/√s for all x,where xs is the best s -sparse approximation of the vector x in l2, σs(x)is the s-sparse approximation error of the vector x in l1 , and D and β are positive constants. The sparse approximation property for a measurement matrix can be thought of as a weaker version of its restricted isometry property and a stronger version of its null space property. In this correspondence, we show that the sparse approximation property is an appropriate condition on a measurement matrix to consider stable recovery of any compressible signal from its noisy measurements. In particular, we show that any compressible signal can be stably recovered from its noisy measurements via solving an l1-minimization problem if the measurement matrix has the sparse approximation property with β ∈ (0,1), and conversely the measurement matrix has the sparse approximation property with β ∈ (0,∞) if any compressible signal can be stably recovered from its noisy measurements via solving an l1 -minimization problem.

Connected through hyperedge HE-1 (induced by source paper "matrix recovery from rank one projection measurements via nonconvex minimization"):
  with members:
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "stable low rank matrix recovery via null space properties"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressive multiplexing of correlated signals"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-2 (induced by source paper "truncated sparse approximation property and truncated q norm minimization"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressive multiplexing of correlated signals"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparse signal reconstruction via iterative support detection"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "signal recovery under cumulative coherence"):
  with members:
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing matrices from fourier matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-4 (induced by source paper "restricted q isometry properties adapted to frames for nonconvex l_q analysis"):
  with members:
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing recovery via nonconvex shrinkage penalties"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "recovery of sparsest signals via ell q minimization"

Connected through hyperedge HE-6 (induced by source paper "nonlinear frames and sparse reconstructions in banach spaces"):
  with members:
    - "does ell_p minimization outperform ell_1 minimization"
    - "spatially distributed sampling and reconstruction"
    - "sampling and galerkin reconstruction in reproducing kernel spaces"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "phase retrieval stability and recovery guarantees"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-7 (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"
    - "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"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "phase transitions for greedy sparse approximation algorithms"

Connected through hyperedge HE-8 (induced by source paper "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"):
  with members:
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "the restricted isometry property for time frequency structured random matrices"
    - "general deviants an analysis of perturbations in compressed sensing"

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)