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: "crafting a toolchain for image restoration by deep reinforcement learning"
  Abstract: We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare a toolbox consisting of small-scale convolutional networks of different complexities and specialized in different tasks. Our method, RL-Restore, then learns a policy to select appropriate tools from the toolbox to progressively restore the quality of a corrupted image. We formulate a step-wise reward function proportional to how well the image is restored at each step to learn the action policy. We also devise a joint learning scheme to train the agent and tools for better performance in handling uncertainty. In comparison to conventional human-designed networks, RL-Restore is capable of restoring images corrupted with complex and unknown distortions in a more parameter-efficient manner using the dynamically formed toolchain.

Connected through hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "collaborative deep reinforcement learning for joint object search"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-3 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "attention based adaptive selection of operations for image restoration in the presence of unknown combined distortions"
    - "mutan multimodal tucker fusion for visual question answering"
    - "single image super resolution via a lightweight residual convolutional neural network"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised video summarization by hierarchical reinforcement learning"):
  with members:
    - "rethinking the evaluation of video summaries"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deep reinforcement learning for visual object tracking in videos"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-6 (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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-7 (induced by source paper "unsupervised multi domain multimodal image to image translation with explicit domain constrained disentanglement"):
  with members:
    - "unsupervised domain specific deblurring via disentangled representations"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "learning to adapt structured output space for semantic segmentation"
    - "multimodal unsupervised image to image translation"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-8 (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"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "hierarchical cellular automata for visual saliency"

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)