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 burst denoising"
  Abstract: Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution.

Connected through hyperedge HE-1 (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-2 (induced by source paper "blind universal bayesian image denoising with gaussian noise level learning"):
  with members:
    - "class aware fully convolutional gaussian and poisson denoising"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "deep learning for image denoising a survey"
    - "unprocessing images for learned raw denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"

Connected through hyperedge HE-3 (induced by source paper "video enhancement with task oriented flow"):
  with members:
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "multi frame quality enhancement for compressed video"
    - "context aware synthesis for video frame interpolation"
    - "flow guided feature aggregation for video object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "accurate optical flow via direct cost volume processing"

Connected through hyperedge HE-4 (induced by source paper "basis prediction networks for effective burst denoising with large kernels"):
  with members:
    - "residual non local attention networks for image restoration"
    - "multi kernel prediction networks for denoising of burst images"
    - "handheld mobile photography in very low light"
    - "iterative residual cnns for burst photography applications"
    - "identifying recurring patterns with deep neural networks for natural image denoising"
    - "non local recurrent network for image restoration"
    - "learning to see in the dark"
    - "blade filter learning for general purpose computational photography"

Connected through hyperedge HE-5 (induced by source paper "joint demosaicing and denoising by overfitting of bursts of raw images"):
  with members:
    - "handheld multi frame super resolution"
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "learning to see in the dark"

Connected through hyperedge HE-6 (induced by source paper "joint demosaicking and denoising by fine tuning of bursts of raw images"):
  with members:
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-7 (induced by source paper "learning deformable kernels for image and video denoising"):
  with members:
    - "learning to see in the dark"
    - "unprocessing images for learned raw denoising"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "deformable convolutional networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-8 (induced by source paper "multi kernel prediction networks for denoising of burst images"):
  with members:
    - "compact and computationally efficient representation of deep neural networks"
    - "iterative residual cnns for burst photography applications"
    - "entropy constrained training of deep neural networks"
    - "learning to see in the dark"
    - "unprocessing images for learned raw denoising"
    - "video frame interpolation via adaptive convolution"
    - "burst denoising with kernel prediction networks"
    - "medical image denoising using convolutional denoising autoencoders"

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)