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: "burst denoising with kernel prediction networks"
  Abstract: We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-2 (induced by source paper "regularized adaptation for stable and efficient continuous level learning"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"
    - "residual dense network for image super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"

Connected through hyperedge HE-3 (induced by source paper "abba saliency regularized motion based adversarial blur attack"):
  with members:
    - "effects of blur and deblurring to visual object tracking"
    - "brain inspired reverse adversarial examples"
    - "spatial aware online adversarial perturbations against visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "amora black box adversarial morphing attack"
    - "adversarial attacks and defences competition"
    - "learning to synthesize motion blur"

Connected through hyperedge HE-4 (induced by source paper "burst denoising of dark images"):
  with members:
    - "end to end denoising of dark burst images using recurrent fully convolutional networks"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"
    - "learning digital camera pipeline for extreme low light imaging"
    - "handheld mobile photography in very low light"
    - "deep retinex decomposition for low light enhancement"
    - "deep sets"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to see in the dark"

Connected through hyperedge HE-5 (induced by source paper "multigrid predictive filter flow for unsupervised learning on videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "deep clustering for unsupervised learning of visual features"
    - "efficient video object segmentation via network modulation"
    - "object discovery in videos as foreground motion clustering"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-6 (induced by source paper "context gated convolution"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "attention augmented convolutional networks"
    - "generating long sequences with sparse transformers"
    - "weight standardization"
    - "eco efficient convolutional network for online video understanding"
    - "bam bottleneck attention module"

Connected through hyperedge HE-7 (induced by source paper "carafe content aware reassembly of features"):
  with members:
    - "deep flow guided video inpainting"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "meta sr a magnification arbitrary network for super resolution"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "foreground aware image inpainting"
    - "mask scoring r cnn"
    - "guided upsampling network for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "toward real world single image super resolution a new benchmark and a new model"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "toward bridging the simulated to real gap benchmarking super resolution on real data"
    - "residual dense network for image super resolution"

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)