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: "pieapp perceptual image error assessment through pairwise preference"
  Abstract: The ability to estimate the perceptual error between images is an important problem in computer vision with many applications. Although it has been studied extensively, however, no method currently exists that can robustly predict visual differences like humans. Some previous approaches used hand-coded models, but they fail to model the complexity of the human visual system. Others used machine learning to train models on human-labeled datasets, but creating large, high-quality datasets is difficult because people are unable to assign consistent error labels to distorted images. In this paper, we present a new learning-based method that is the first to predict perceptual image error like human observers. Since it is much easier for people to compare two given images and identify the one more similar to a reference than to assign quality scores to each, we propose a new, large-scale dataset labeled with the probability that humans will prefer one image over another. We then train a deep-learning model using a novel, pairwise-learning framework to predict the preference of one distorted image over the other. Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels. The perceptual error estimated by our new metric, PieAPP, is well-correlated with human opinion. Furthermore, it significantly outperforms existing algorithms, beating the state-of-the-art by almost 3x on our test set in terms of binary error rate, while also generalizing to new kinds of distortions, unlike previous learning-based methods.

Connected through hyperedge HE-1 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  with members:
    - "i am going mad maximum discrepancy competition for comparing classifiers adaptively"
    - "learning to blindly assess image quality in the laboratory and wild"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (induced by source paper "a deep journey into super resolution a survey"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "single image super resolution via cascaded multi scale cross network"
    - "fast and accurate single image super resolution via information distillation network"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "deep underwater image enhancement"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-3 (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"
    - "learned perceptual image enhancement"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-4 (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"
    - "learned perceptual image enhancement"
    - "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-5 (induced by source paper "approximating human judgment of generated image quality"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "gansynth adversarial neural audio synthesis"
    - "pros and cons of gan evaluation measures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generating adversarial examples with adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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"
    - "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"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-7 (induced by source paper "on hallucinating context and background pixels from a face mask using multi scale gans"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "faceshop deep sketch based face image editing"
    - "fast face image synthesis with minimal training"
    - "identity preserving face completion for large ocular region occlusion"
    - "unified perceptual parsing for scene understanding"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-8 (induced by source paper "learning temporal coherence via self supervision for gan based video generation"):
  with members:
    - "mocycle gan unpaired video to video translation"
    - "learning correspondence from the cycle consistency of time"
    - "preserving semantic and temporal consistency for unpaired video to video translation"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"

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)