human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "classification driven dynamic image enhancement"):
  Abstract: Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as preprocessing steps to help improve the overall image quality and in turn improve the overall effectiveness of a CNN. Existing image enhancement methods, however, are designed to improve the perceptual quality of an image for a human observer. In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception. To this end, we present a unified CNN architecture that uses a range of enhancement filters that can enhance image-specific details via end-to-end dynamic filter learning. We demonstrate the effectiveness of this strategy on four challenging benchmark datasets for fine-grained, object, scene, and texture classification: CUB-200-2011, PASCAL-VOC2007, MIT-Indoor, and DTD. Experiments using our proposed enhancement show promising results on all the datasets. In addition, our approach is capable of improving the performance of all generic CNN architectures.

Includes members:
  Member "clearing the skies a deep network architecture for single image rain removal":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "deep class aware denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "external patch based image restoration using importance sampling")
  Member "fast image processing with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "getting to 99 accuracy in interactive segmentation")
  Member "examining the impact of blur on recognition by convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmarking the robustness of semantic segmentation models")
  Member "an empirical study on the effects of different types of noise in image classification tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples")
  Member "how image degradations affect deep cnn based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "places an image database for deep scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural architecture search for deep image prior")
  Member "does v nir based image enhancement come with better features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "can image enhancement be beneficial to find smoke images in laparoscopic surgery")

Question: Please tell me which class the source paper 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)