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: "gradient magnitude similarity deviation a highly efficient perceptual image quality index"
  Abstract: It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy the standard deviation of the GMS map can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "subjective annotation for a frame interpolation benchmark using artifact amplification"):
  with members:
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame synthesis using deep voxel flow"
    - "qoe beyond the mos an in depth look at qoe via better metrics and their relation to mos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-2 (induced by source paper "quality aware unpaired image to image translation"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual adversarial networks for image to image transformation"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "from patches to pictures paq 2 piq mapping the perceptual space of picture quality"):
  with members:
    - "learning to blindly assess image quality in the laboratory and wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "nima neural image assessment"
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "on the use of deep learning for blind image quality assessment"

Connected through hyperedge HE-4 (induced by source paper "scoot a perceptual metric for facial sketches"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a multilayer based framework for online background subtraction with freely moving cameras"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "random sampling for fast face sketch synthesis"

Connected through hyperedge HE-5 (induced by source paper "fastderain a novel video rain streak removal method using directional gradient priors"):
  with members:
    - "learning dual convolutional neural networks for low level vision"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "density aware single image de raining using a multi stream dense network"
    - "desnownet context aware deep network for snow removal"
    - "a novel nonconvex approach to recover the low tubal rank tensor data when t svd meets pssv"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "benchmarking single image dehazing and beyond"

Connected through hyperedge HE-6 (induced by source paper "jnd salcar a novel jnd based saliency channel attention residual network for image quality prediction"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "image super resolution using very deep residual channel attention networks"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "saliency detection based on structural dissimilarity induced by image quality assessment model"):
  with members:
    - "deep visual attention prediction"
    - "what catches the eye visualizing and understanding deep saliency models"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "visual saliency based on scale space analysis in the frequency domain"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "total variation with overlapping group sparsity and lp quasinorm for infrared image deblurring under salt and pepper noise"):
  with members:
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "group sparse signal denoising non convex regularization convex optimization"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "image restoration using total variation with overlapping group sparsity"

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)