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: "enhanced low rank matrix approximation"
  Abstract: This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with nonconvex regularization. We employ parameterized nonconvex penalty functions to estimate the nonzero singular values more accurately than the nuclear norm. A closed-form solution for the global optimum of the proposed objective function (sum of data fidelity and the nonconvex regularizer) is also derived. The solution reduces to singular value thresholding method as a special case. The proposed method is demonstrated for image denoising.

Connected through hyperedge HE-1 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "learning deep cnn denoiser prior for image restoration"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-2 (induced by source paper "ell_0 motivated low rank sparse subspace clustering"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "multi view low rank sparse subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "improving compressed sensing with the diamond norm"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "generalized singular value thresholding"

Connected through hyperedge HE-3 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-4 (induced by source paper "nonconvex penalties with analytical solutions for one bit compressive sensing"):
  with members:
    - "pinball loss minimization for one bit compressive sensing convex models and algorithms"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "decreasing weighted sorted ell_1 regularization"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"
    - "robust pca via nonconvex rank approximation"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

Connected through hyperedge HE-6 (induced by source paper "a penalty function promoting sparsity within and across groups"):
  with members:
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "a class of nonconvex penalties preserving overall convexity in optimization based mean filtering"):
  with members:
    - "successive concave sparsity approximation for compressed sensing"
    - "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"

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.CV(Computer Vision and Pattern Recognition)