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: "rankiqa learning from rankings for no reference image quality assessment"
  Abstract: We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch. Experiments on the TID2013 benchmark show that we improve the state-of-the-art by over 5%. Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.

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"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "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 "make a face towards arbitrary high fidelity face manipulation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "on learning 3d face morphable model from in the wild images"
    - "pose guided human video generation"
    - "look at boundary a boundary aware face alignment algorithm"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "self supervised learning of a facial attribute embedding from video"
    - "blind predicting similar quality map for image quality assessment"

Connected through hyperedge HE-3 (induced by source paper "exploiting unlabeled data in cnns by self supervised learning to rank"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "on the use of deep learning for blind image quality assessment"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-4 (induced by source paper "ranksrgan generative adversarial networks with ranker for image super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "suppressing model overfitting for image super resolution networks"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-5 (induced by source paper "learning metrics from teachers compact networks for image embedding"):
  with members:
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "metric learning for novelty and anomaly detection"
    - "learning deep representations with probabilistic knowledge transfer"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-6 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "high fidelity image generation with fewer labels"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "aesthetic based clothing recommendation"
    - "tracking emerges by colorizing videos"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"

Connected through hyperedge HE-7 (induced by source paper "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"):
  with members:
    - "learning to blindly assess image quality in the laboratory and wild"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "learning transferable architectures for scalable image recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to detect multiple photographic defects"
    - "understanding how image quality affects deep neural networks"
    - "massive online crowdsourced study of subjective and objective picture quality"

Connected through hyperedge HE-8 (induced by source paper "self supervised blur detection from synthetically blurred scenes"):
  with members:
    - "single image depth estimation trained via depth from defocus cues"
    - "learning to adapt structured output space for semantic segmentation"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "self supervised feature learning by learning to spot artifacts"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"

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)