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: "on the use of deep learning for blind image quality assessment"
  Abstract: In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a linear correlation coefficient with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008, and TID2013.

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"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "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 "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"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "generating high quality crowd density maps using contextual pyramid cnns"

Connected through hyperedge HE-3 (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"
    - "learning transferable architectures for scalable image recognition"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "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-4 (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"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-5 (induced by source paper "real time quality assessment of pediatric mri via semi supervised deep nonlocal residual neural networks"):
  with members:
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "learning from noisy labels with distillation"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"
    - "xception deep learning with depthwise separable convolutions"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-6 (induced by source paper "an universal image attractiveness ranking framework"):
  with members:
    - "nima neural image assessment"
    - "massive online crowdsourced study of subjective and objective picture quality"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "hallucinated iqa no reference image quality assessment via adversarial learning"):
  with members:
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "quality aware network for set to set recognition"
    - "weakly and self supervised learning for content aware deep image retargeting"
    - "learning deep cnn denoiser prior for image restoration"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "stacked hourglass networks for human pose estimation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-8 (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"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "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"

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)