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: "aesthetic driven image enhancement by adversarial learning"
  Abstract: We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement.

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 "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "high fidelity image generation with fewer labels"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "aesthetic based clothing recommendation"
    - "tracking emerges by colorizing videos"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"

Connected through hyperedge HE-3 (induced by source paper "grid anchor based image cropping a new benchmark and an efficient model"):
  with members:
    - "reliable and efficient image cropping a grid anchor based approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "nima neural image assessment"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "image aesthetic assessment an experimental survey"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to compose with professional photographs on the web"

Connected through hyperedge HE-4 (induced by source paper "a coarse to fine framework for learned color enhancement with non local attention"):
  with members:
    - "distort and recover color enhancement using deep reinforcement learning"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "non local neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "fast image processing with fully convolutional networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "reliable and efficient image cropping a grid anchor based approach"):
  with members:
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "image aesthetic assessment an experimental survey"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to compose with professional photographs on the web"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "mask r cnn"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "theme aware aesthetic distribution prediction with full resolution photos"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "cascade r cnn delving into high quality object detection"
    - "deep aesthetic quality assessment with semantic information"
    - "mask r cnn"
    - "r fcn object detection via region based fully convolutional networks"
    - "fast r cnn"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "dr net transmission steered single image dehazing network with weakly supervised refinement"):
  with members:
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "joint transmission map estimation and dehazing using deep networks"
    - "dehazenet an end to end system for single image haze removal"
    - "haze visibility enhancement a survey and quantitative benchmarking"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

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)