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 the stable recovery of the sparsest overcomplete representations in presence of noise"
  Abstract: Let x be a signal to be sparsely decomposed over a redundant dictionary A, i.e., a sparse coefficient vector s has to be found such that x = As. It is known that this problem is inherently unstable against noise, and to overcome this instability, Donoho, Elad and Temlyakov ["Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. Inf. Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006] have proposed to use an "approximate" decomposition, that is, a decomposition satisfying ∥x - As∥2 ≤ δ rather than satisfying the exact equality x = As. Then, they have shown that if there is a decomposition with ∥s∥0 <;; (1 + M-1)/2, where M denotes the coherence of the dictionary, this decomposition would be stable against noise. On the other hand, it is known that a sparse decomposition with ∥s∥0 <;; (1/2)spark(A) is unique. In other words, although a decomposition with ∥s∥0 <;; (1/2)spark(A) is unique, its stability against noise has been proved only for highly more restrictive decompositions satisfying ∥s∥0 <;; (1 + M-1)/2, because usually (1 + M-1)/2 ≪ (1/2)spark(A). This limitation maybe had not been very important before, because ∥s∥0 <;; (1 + M-1)/2 is also the bound which guaranties that the sparse decomposition can be found via minimizing the l1 norm, a classic approach for sparse decomposition. However, with the availability of new algorithms for sparse decomposition, namely SL0 and robust-SLO, it would be important to know whether or not unique sparse decompositions with (1 + M-1)/2 ≤ ∥s∥0 <;; (1/2)spark(A) are stable. In this correspondence, we show that such decompositions are indeed stable. In other words, we extend the stability bound from ∥s∥0 <;; (1 + M-1)/2 to the whole uniqueness range ∥s∥0 <;; (1/2)spark(A). In summary, we show that all unique sparse decompositions are stably recoverable. Moreover, we see that sparser decompositions are "more stable".

Connected through hyperedge HE-1 (induced by source paper "successive concave sparsity approximation for compressed sensing"):
  with members:
    - "iterative concave rank approximation for recovering low rank matrices"
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "robustness of sparse recovery via f minimization a topological viewpoint"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "approximate projected generalized gradient methods with sparsity inducing penalties"
    - "the convergence guarantees of a non convex approach for sparse recovery"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "optimal phase transitions in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-3 (induced by source paper "successive concave sparsity approximation near oracle performance in a wide range of sparsity levels"):
  with members:
    - "iterative concave rank approximation for recovering low rank matrices"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "on the error of estimating the sparsest solution of underdetermined linear systems"):
  with members:
    - "sparse signal reconstruction via iterative support detection"
    - "sequential compressed sensing"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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)