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: "deep convolution networks for compression artifacts reduction"
  Abstract: Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened images that are accompanied with ringing effects. Inspired by the success of deep convolutional networks (DCN) on superresolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. To meet the speed requirement of real-world applications, we further accelerate the proposed baseline model by layer decomposition and joint use of large-stride convolutional and deconvolutional layers. This also leads to a more general CNN framework that has a close relationship with the conventional Multi-Layer Perceptron (MLP). Finally, the modified network achieves a speed up of 7.5 times with almost no performance loss compared to the baseline model. We also demonstrate that a deeper model can be effectively trained with features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate three practical transfer settings and show the effectiveness of transfer learning in low-level vision problems. Our method shows superior performance than the state-of-the-art methods both on benchmark datasets and a real-world use case.

Connected through hyperedge HE-1 (induced by source paper "compressing weight updates for image artifacts removal neural networks"):
  with members:
    - "compressibility loss for neural network weights"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
    - "u net convolutional networks for biomedical image segmentation"
    - "compression artifacts reduction by a deep convolutional network"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-2 (induced by source paper "s net a scalable convolutional neural network for jpeg compression artifact reduction"):
  with members:
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "enhanced deep residual networks for single image super resolution"
    - "single image super resolution with dilated convolution based multi scale information learning inception module"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "wide residual networks"

Connected through hyperedge HE-3 (induced by source paper "non blind image restoration based on convolutional neural network"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "accurate image super resolution using very deep convolutional networks"
    - "deeply recursive convolutional network for image super resolution"
    - "adam a method for stochastic optimization"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "a new convolutional network in network structure and its applications in skin detection semantic segmentation and artifact reduction"):
  with members:
    - "compression artifacts removal using convolutional neural networks"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "learning deconvolution network for semantic segmentation"
    - "compression artifacts reduction by a deep convolutional network"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "cas cnn a deep convolutional neural network for image compression artifact suppression"):
  with members:
    - "compression artifacts removal using convolutional neural networks"
    - "deep residual learning for image recognition"
    - "deeply recursive convolutional network for image super resolution"
    - "learning deconvolution network for semantic segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "compression artifacts reduction by a deep convolutional network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic 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)