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: "renoir a dataset for real low light image noise reduction"
  Abstract: Abstract   Image denoising algorithms are evaluated using images corrupted by artificial noise, which may lead to incorrect conclusions about their performances on real noise. In this paper we introduce a dataset of color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes. We also introduce a method for estimating the true noise level in our images, since even the low noise images contain small amounts of noise. We evaluate the accuracy of our noise estimation method on real and artificial noise, and investigate the Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer Perceptron, and two versions of NL-means. We show that while the Multi-Layer Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on gray images with synthetic noise, they lag behind on our dataset.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "noise flow noise modeling with conditional normalizing flows"
    - "real image denoising with feature attention"
    - "semantic image synthesis with spatially adaptive normalization"
    - "suppressing model overfitting for image super resolution networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-2 (induced by source paper "image restoration for under display camera"):
  with members:
    - "when awgn based denoiser meets real noises"
    - "dense haze a benchmark for image dehazing with dense haze and haze free images"
    - "noise flow noise modeling with conditional normalizing flows"
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "learning to synthesize motion blur"

Connected through hyperedge HE-3 (induced by source paper "nlh a blind pixel level non local method for real world image denoising"):
  with members:
    - "progressive image deraining networks a better and simpler baseline"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "toward convolutional blind denoising of real photographs"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"

Connected through hyperedge HE-4 (induced by source paper "path restore learning network path selection for image restoration"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "skipnet learning dynamic routing in convolutional networks"
    - "blockdrop dynamic inference paths in residual networks"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (induced by source paper "real image denoising with feature attention"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"
    - "unprocessing images for learned raw denoising"
    - "chaining identity mapping modules for image denoising"

Connected through hyperedge HE-6 (induced by source paper "variational denoising network toward blind noise modeling and removal"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "residual dense network for image super resolution"
    - "unprocessing images for learned raw denoising"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-7 (induced by source paper "spatial attentive single image deraining with a high quality real rain dataset"):
  with members:
    - "residual guide feature fusion network for single image deraining"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "density aware single image de raining using a multi stream dense network"
    - "vital visual tracking via adversarial learning"
    - "what can help pedestrian detection"
    - "direction aware spatial context features for shadow detection"

Connected through hyperedge HE-8 (induced by source paper "videnn deep blind video denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "phasenet for video frame interpolation"
    - "toward convolutional blind denoising of real photographs"
    - "noise2noise learning image restoration without clean data"
    - "context aware synthesis for video frame interpolation"
    - "learning to see in the dark"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

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)