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: "a nonlocal structure tensor based approach for multicomponent image recovery problems"
  Abstract: Nonlocal total variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the structure tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the nonlocal variations, jointly for the different components, through various l#N# 1,p#N#-matrix-norms with p ≥ 1. To facilitate the choice of the hyperparameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented because of the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for color, multispectral, and hyperspectral images. The results demonstrate the interest of introducing a nonlocal ST regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods, such as the alternating direction method of multipliers.

Connected through hyperedge HE-1 (induced by source paper "orthonet multilayer network data clustering"):
  with members:
    - "on the convergence of adam and beyond"
    - "spectral inference networks unifying deep and spectral learning"
    - "stochastic gradient descent for spectral embedding with implicit orthogonality constraint"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"
    - "gemsec graph embedding with self clustering"
    - "non local neural networks"
    - "representation learning on graphs methods and applications"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "blind restoration for nonuniform aerial images using nonlocal retinex model and shearlet based higher order regularization"):
  with members:
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "group based sparse representation for image restoration"
    - "bi l0 l2 norm regularization for blind motion deblurring"

Connected through hyperedge HE-3 (induced by source paper "marlow a joint multiplanar autoregressive and low rank approach for image completion"):
  with members:
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "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)