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: "mean deviation similarity index efficient and reliable full reference image quality evaluator"
  Abstract: Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration, and multimedia communication, have led to the development of many IQA metrics. In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP). By considering the shortcomings of the commonly used GS to model the human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS. We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes. In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions. Motivated by a recent work that utilizes the standard DP, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps. This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well. The experimental results on six data sets of natural images, a synthetic data set, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity. The MATLAB source code of the proposed metric is available at  https://dl.dropboxusercontent.com/u/74505502/MDSI.m .

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"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"

Connected through hyperedge HE-2 (induced by source paper "sur featnet predicting the satisfied user ratio curvefor image compression with deep feature learning"):
  with members:
    - "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "focal loss for dense object detection"
    - "videoset a large scale compressed video quality dataset based on jnd measurement"
    - "prediction of satisfied user ratio for compressed video"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "real time quality assessment of pediatric mri via semi supervised deep nonlocal residual neural networks"):
  with members:
    - "on the use of deep learning for blind image quality assessment"
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "learning from noisy labels with distillation"
    - "xception deep learning with depthwise separable convolutions"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-4 (induced by source paper "computer vision and metrics learning for hypothesis testing an application of q q plot for normality test"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-5 (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"
    - "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"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "efficient no reference quality assessment and classification model for contrast distorted images"):
  with members:
    - "learning a no reference quality assessment model of enhanced images with big data"
    - "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)