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: "generalized deep image to image regression"
  Abstract: We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery. Our proposed architecture: the Recursively Branched Deconvolutional Network (RBDN) develops a cheap multi-context image representation very early on using an efficient recursive branching scheme with extensive parameter sharing and learnable upsampling. This multi-context representation is subjected to a highly non-linear locality preserving transformation by the remainder of our network comprising of a series of convolutions/deconvolutions without any spatial downsampling. The RBDN architecture is fully convolutional and can handle variable sized images during inference. We provide qualitative/quantitative results on $3$ diverse tasks: relighting, denoising and colorization and show that our proposed RBDN architecture obtains comparable results to the state-of-the-art on each of these tasks when used off-the-shelf without any post processing or task-specific architectural modifications.

Connected through hyperedge HE-1 (induced by source paper "multi level wavelet convolutional neural networks"):
  with members:
    - "parallel grid pooling for data augmentation"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "visualized insights into the optimization landscape of fully convolutional networks"):
  with members:
    - "understanding the loss surface of neural networks for binary classification"
    - "optimization landscape and expressivity of deep cnns"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "richer convolutional features for edge detection"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "depth creates no bad local minima"

Connected through hyperedge HE-3 (induced by source paper "when awgn based denoiser meets real noises"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "path restore learning network path selection for image restoration"
    - "real image denoising with feature attention"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "survey of face detection on low quality images"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "connecting image denoising and high level vision tasks via deep learning"

Connected through hyperedge HE-4 (induced by source paper "fastdvdnet towards real time video denoising without explicit motion estimation"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "non local video denoising by cnn"
    - "video object segmentation with language referring expressions"
    - "learning to see in the dark"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "multi level wavelet cnn for image restoration"
    - "deep high dynamic range imaging with large foreground motions"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-5 (induced by source paper "multi level wavelet cnn for image restoration"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "memnet a persistent memory network for image restoration"
    - "learning convolutional networks for content weighted image compression"

Connected through hyperedge HE-6 (induced by source paper "ffdnet toward a fast and flexible solution for cnn based image denoising"):
  with members:
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning non local image diffusion for image denoising"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "memnet a persistent memory network for image restoration"
    - "diracnets training very deep neural networks without skip connections"

Connected through hyperedge HE-7 (induced by source paper "non local video denoising by cnn"):
  with members:
    - "frame recurrent video super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "learning to see in the dark"
    - "deep burst denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-8 (induced by source paper "direction aware spatial context features for shadow detection and removal"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "intrinsic image transformation via scale space decomposition"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "direction aware spatial context features for shadow detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "training region based object detectors with online hard example mining"
    - "pyramid scene parsing network"

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)