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 bilateral learning for real time image enhancement"
  Abstract: Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.

Connected through hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "kindling the darkness a practical low light image enhancer"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "a deep journey into super resolution a survey"
    - "selective kernel networks"
    - "deep learning for image super resolution a survey"
    - "real image denoising with feature attention"

Connected through hyperedge HE-2 (induced by source paper "blind image restoration without prior knowledge"):
  with members:
    - "blind universal bayesian image denoising with gaussian noise level learning"
    - "blind super resolution with iterative kernel correction"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-3 (induced by source paper "real time image smoothing via iterative least squares"):
  with members:
    - "quantitative evaluation of base and detail decomposition filters based on their artifacts"
    - "semi global weighted least squares in image filtering"
    - "image to image translation with conditional adversarial networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing"):
  with members:
    - "weakly supervised video summarization by hierarchical reinforcement learning"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "ffnet video fast forwarding via reinforcement learning"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-6 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"

Connected through hyperedge HE-7 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-8 (induced by source paper "learning task specific generalized convolutions in the permutohedral lattice"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic filtering with large sampling field for convnets"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"
    - "segmentation aware convolutional networks using local attention masks"
    - "sampling matters in deep embedding learning"

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.GR(Graphics)