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: "deeply aggregated alternating minimization for image restoration"
  Abstract: Regularization-based image restoration has remained an active research topic in computer vision and image processing. It often leverages a guidance signal captured in different fields as an additional cue. In this work, we present a general framework for image restoration, called deeply aggregated alternating minimization (DeepAM). We propose to train deep neural network to advance two of the steps in the conventional AM algorithm: proximal mapping and ?- continuation. Both steps are learned from a large dataset in an end-to-end manner. The proposed framework enables the convolutional neural networks (CNNs) to operate as a prior or regularizer in the AM algorithm. We show that our learned regularizer via deep aggregation outperforms the recent data-driven approaches as well as the nonlocalbased methods. The flexibility and effectiveness of our framework are demonstrated in several image restoration tasks, including single image denoising, RGB-NIR restoration, and depth super-resolution.

Connected through hyperedge HE-1 (induced by source paper "denoising prior driven deep neural network for image restoration"):
  with members:
    - "deep learning for generic object detection a survey"
    - "a survey of recent advances in texture representation"
    - "image super resolution via dual state recurrent networks"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-2 (induced by source paper "connecting image denoising and high level vision tasks via deep learning"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "class aware fully convolutional gaussian and poisson denoising"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "non local recurrent network for image restoration"
    - "on the relation between color image denoising and classification"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"

Connected through hyperedge HE-3 (induced by source paper "a multiscale image denoising algorithm based on dilated residual convolution network"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dilated deep residual network for image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "dilated residual networks"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"

Connected through hyperedge HE-4 (induced by source paper "image correction via deep reciprocating hdr transformation"):
  with members:
    - "stylizing face images via multiple exemplars"
    - "msr net low light image enhancement using deep convolutional network"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning high dynamic range from outdoor panoramas"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "learning deep cnn denoiser prior for image restoration"
    - "segflow joint learning for video object segmentation and optical flow"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-5 (induced by source paper "deep faster detection of faint edges in noisy images"):
  with members:
    - "the perception distortion tradeoff"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "fast detection of curved edges at low snr"
    - "holistically nested edge detection"

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)