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: "learned perceptual image enhancement"
  Abstract: Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhancement operators. This metric is implemented using a CNN (convolutional neural network) trained on a large-scale dataset labelled with aesthetic preferences of human raters. This loss allows us to conveniently perform back-propagation in our learning framework to simultaneously optimize for similarity to a given ground truth reference and perceptual quality. This perceptual loss is only used to train parameters of image processing operators, and does not impose any extra complexity at inference time. Our experiments demonstrate that this loss can be effective for tuning a variety of operators such as local tone mapping and dehazing.

Connected through hyperedge HE-1 (induced by source paper "super resolving commercial satellite imagery using realistic training data"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "camera lens super resolution"
    - "nima neural image assessment"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "super resolution for overhead imagery using densenets and adversarial learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "learning digital camera pipeline for extreme low light imaging"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep exemplar based colorization"
    - "learning to see in the dark"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-3 (induced by source paper "learning similarity metrics for numerical simulations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "siamese networks for semantic pattern similarity"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

Connected through hyperedge HE-4 (induced by source paper "learning to model aspects of hearing perception using neural loss functions"):
  with members:
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "conditional end to end audio transforms"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "audio adversarial examples targeted attacks on speech to text"
    - "tacotron towards end to end speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "the unreasonable effectiveness of deep features as a perceptual metric"):
  with members:
    - "nima neural image assessment"
    - "enhanced deep residual networks for single image super resolution"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "video frame interpolation via adaptive separable convolution"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning representations for automatic colorization"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "nima neural image assessment"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "on the use of deep learning for blind image quality assessment"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a color intensity invariant low level feature optimization framework for image quality assessment"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "brain inspired deep networks for image aesthetics assessment"
    - "squared earth mover s distance based loss for training deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "a gated peripheral foveal convolutional neural network for unified image aesthetic prediction"):
  with members:
    - "deep visual attention prediction"
    - "nima neural image assessment"
    - "robust deep multi modal learning based on gated information fusion network"
    - "a deep architecture for unified aesthetic prediction"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "deep aesthetic quality assessment with semantic information"
    - "squeeze and excitation networks"
    - "top down neural attention by excitation backprop"

Connected through hyperedge HE-8 (induced by source paper "quality net an end to end non intrusive speech quality assessment model based on blstm"):
  with members:
    - "nima neural image assessment"
    - "fast and accurate deep network learning by exponential linear units elus"

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)