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: "successive concave sparsity approximation for compressed sensing"
  Abstract: In this paper, based on a successively accuracy-increasing approximation of the    $\ell _0$   norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the    $\ell _0$   norm can be controlled. We prove that the series of the approximations asymptotically coincides with the   $\ell _1$   and   $\ell _0$   norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed that leads to a number of weighted   $\ell _1$    minimization programs, whereas, in the presence of noise, we propose two iterative thresholding methods that are computationally appealing. A convergence guarantee for the iterative thresholding method is provided, and, for a particular function in the class of the approximating functions, we derive the closed-form thresholding operator. We further present some theoretical analyses via the restricted isometry, null space, and spherical section properties. Our extensive numerical simulations indicate that the proposed algorithm closely follows the performance of the oracle estimator for a range of sparsity levels wider than those of the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "a class of nonconvex penalties preserving overall convexity in optimization based mean filtering"):
  with members:
    - "enhanced low rank matrix approximation"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"

Connected through hyperedge HE-2 (induced by source paper "upper bounds on the error of sparse vector and low rank matrix recovery"):
  with members:
    - "iterative concave rank approximation for recovering low rank matrices"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "on the error of estimating the sparsest solution of underdetermined linear systems"
    - "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)