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: "a haar wavelet based perceptual similarity index for image quality assessment"
  Abstract: Abstract   In most practical situations, the compression or transmission of images and videos creates distortions that will eventually be perceived by a human observer. Vice versa, image and video restoration techniques, such as inpainting or denoising, aim to enhance the quality of experience of human viewers. Correctly assessing the similarity between an image and an undistorted reference image as subjectively experienced by a human viewer can thus lead to significant improvements in any transmission, compression, or restoration system. This paper introduces the Haar wavelet-based perceptual similarity index (HaarPSI), a novel and computationally inexpensive similarity measure for full reference image quality assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. The consistency of the HaarPSI with the human quality of experience was validated on four large benchmark databases containing thousands of differently distorted images. On these databases, the HaarPSI achieves higher correlations with human opinion scores than state-of-the-art full reference similarity measures like the structural similarity index (SSIM), the feature similarity index (FSIM), and the visual saliency-based index (VSI). Along with the simple computational structure and the short execution time, these experimental results suggest a high applicability of the HaarPSI in real world tasks.

Connected through hyperedge HE-1 (induced by source paper "the oracle of dlphi"):
  with members:
    - "nfft meets krylov methods fast matrix vector products for the graph laplacian of fully connected networks"
    - "adversarial regularizers in inverse problems"
    - "the universal approximation power of finite width deep relu networks"
    - "mathematics of deep learning"
    - "underwater object classification using scattering transform of sonar signals"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "error bounds for approximations with deep relu networks"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-3 (induced by source paper "a tree based dictionary learning framework"):
  with members:
    - "a region based easy path wavelet transform for sparse image representation"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "on sparse representation in fourier and local bases"
    - "the haar wavelet transform of a dendrogram"

Connected through hyperedge HE-4 (induced by source paper "pt mmd a novel statistical framework for the evaluation of generative systems"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-5 (induced by source paper "iegan multi purpose perceptual quality image enhancement using generative adversarial network"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "compression artifacts removal using convolutional neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image to image translation with conditional adversarial networks"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-6 (induced by source paper "blind predicting similar quality map for image quality assessment"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "on the use of deep learning for blind image quality assessment"
    - "focal loss for dense object detection"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "mask r cnn"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-7 (induced by source paper "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "multi scale wavelet domain residual learning for limited angle ct reconstruction"
    - "deep convolutional neural network for inverse problems in imaging"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"

Connected through hyperedge HE-8 (induced by source paper "deep neural networks for no reference and full reference image quality assessment"):
  with members:
    - "massive online crowdsourced study of subjective and objective picture quality"
    - "fast r cnn"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

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)