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: "convolutional neural pyramid for image processing"
  Abstract: We propose a principled convolutional neural pyramid (CNP) framework for general low-level vision and image processing tasks. It is based on the essential finding that many applications require large receptive fields for structure understanding. But corresponding neural networks for regression either stack many layers or apply large kernels to achieve it, which is computationally very costly. Our pyramid structure can greatly enlarge the field while not sacrificing computation efficiency. Extra benefit includes adaptive network depth and progressive upsampling for quasi-realtime testing on VGA-size input. Our method profits a broad set of applications, such as depth/RGB image restoration, completion, noise/artifact removal, edge refinement, image filtering, image enhancement and colorization.

Connected through hyperedge HE-1 (induced by source paper "multimedia super resolution via deep learning a survey"):
  with members:
    - "joint convolutional neural pyramid for depth map super resolution"
    - "learning a mixture of deep networks for single image super resolution"
    - "an analysis of deep neural network models for practical applications"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "failures of gradient based deep learning"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "single image super resolution when model adaptation matters"

Connected through hyperedge HE-2 (induced by source paper "exfuse enhancing feature fusion for semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize"
    - "squeeze and excitation networks"
    - "understanding convolution for semantic segmentation"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"

Connected through hyperedge HE-3 (induced by source paper "lightweight pyramid networks for image deraining"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "image de raining using a conditional generative adversarial network"
    - "memnet a persistent memory network for image restoration"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "ensnet ensconce text in the wild"):
  with members:
    - "dssd deconvolutional single shot detector"
    - "image de raining using a conditional generative adversarial network"
    - "scene text eraser"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "synthetic data for text localisation in natural images"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-5 (induced by source paper "deep texture and structure aware filtering network for image smoothing"):
  with members:
    - "co occurrence filter"
    - "semi global weighted least squares in image filtering"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "fast image processing with fully convolutional networks"
    - "dehazenet an end to end system for single image haze removal"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "holistically nested edge detection"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)