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: "recovery of low rank matrices under affine constraints via a smoothed rank function"
  Abstract: In this paper, the problem of matrix rank minimization under affine constraints is addressed. The state-of-the-art algorithms can recover matrices with a rank much less than what is sufficient for the uniqueness of the solution of this optimization problem. We propose an algorithm based on a smooth approximation of the rank function, which practically improves recovery limits on the rank of the solution. This approximation leads to a non-convex program; thus, to avoid getting trapped in local solutions, we use the following scheme. Initially, a rough approximation of the rank function subject to the affine constraints is optimized. As the algorithm proceeds, finer approximations of the rank are optimized and the solver is initialized with the solution of the previous approximation until reaching the desired accuracy. On the theoretical side, benefiting from the spherical section property, we will show that the sequence of the solutions of the approximating programs converges to the minimum rank solution. On the experimental side, it will be shown that the proposed algorithm, termed SRF standing for smoothed rank function, can recover matrices, which are unique solutions of the rank minimization problem and yet not recoverable by nuclear norm minimization. Furthermore, it will be demonstrated that, in completing partially observed matrices, the accuracy of SRF is considerably and consistently better than some famous algorithms when the number of revealed entries is close to the minimum number of parameters that uniquely represent a low-rank matrix.

Connected through hyperedge HE-1 (induced by source paper "deep mr image super resolution using structural priors"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "accurate image super resolution using very deep convolutional networks"
    - "deeply recursive convolutional network for image super resolution"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-2 (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"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "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-3 (induced by source paper "a class of nonconvex penalties preserving overall convexity in optimization based mean filtering"):
  with members:
    - "enhanced low rank matrix approximation"
    - "successive concave sparsity approximation for compressed sensing"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"

Connected through hyperedge HE-4 (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"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "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-5 (induced by source paper "upper bounds on the error of sparse vector and low rank matrix recovery"):
  with members:
    - "successive concave sparsity approximation for compressed sensing"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "iterative concave rank approximation for recovering low rank matrices"):
  with members:
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "calibration using matrix completion with application to ultrasound tomography"
    - "matrix completion with noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "exact matrix completion via convex optimization"

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)