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: "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning"
  Abstract: Both sparse coding and rank minimization have led to great successes in various image processing tasks. Though the underlying principles of these two approaches are similar, no theory is available to demonstrate the correspondence. In this paper, starting by designing an adaptive dictionary for each group of image patches, we analyze the sparsity of image patches in each group using the rank minimization approach. Based on this, we prove that the group-based sparse coding is equivalent to the rank minimization problem under our proposed adaptive dictionary. Therefore, the sparsity of the coefficients for each group can be measured by estimating the singular values of this group. Inspired by our theoretical analysis, four nuclear norm like minimization methods including the standard nuclear norm minimization (NNM), weighted nuclear norm minimization (WNNM), Schatten $p$-norm minimization (SNM), and weighted Schatten $p$-norm minimization (WSNM), are employed to analyze the sparsity of the coefficients and WSNM is found to be the closest solution to the singular values of each group. Based on this, WSNM is then translated to a non-convex weighted $\ell_p$-norm minimization problem in group-based sparse coding, and in order to solve this problem, a new algorithm based on the alternating direction method of multipliers (ADMM) framework is developed. Experimental results on two low-level vision tasks: image inpainting and image compressive sensing recovery, demonstrate that the proposed scheme is feasible and outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "dual reweighted lp norm minimization for salt and pepper noise removal"):
  with members:
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "image denoising using new adaptive based median filters"
    - "generalized singular value thresholding"
    - "stable principal component pursuit"
    - "robust principal component analysis"
    - "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.CV(Computer Vision and Pattern Recognition)