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: "deep optimization model for screen content image quality assessment using neural networks"
  Abstract: In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference and no-reference screen content image (SCI) quality assessment. Unlike traditional CNN methods taking all image patches as training data and using average quality pooling, our model is optimized to obtain a more effective model including three steps. In the first step, an end-to-end deep CNN is trained to preliminarily predict the image visual quality, and batch normalized (BN) layers and l2 regularization are employed to improve the speed and performance of network fitting. For second step, the pretrained model is fine-tuned to achieve better performance under analysis of the raw training data. An adaptive weighting method is proposed in the third step to fuse local quality inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to image patches containing texture and edge information. The novelty of our algorithm can be concluded as follows: 1) with the consideration of correlation between local quality and subjective differential mean opinion score (DMOS), the Euclidean distance is utilized to measure effectiveness of image patches, and the pretrained model is fine-tuned with more effective training data; 2) an adaptive pooling approach is employed to fuse patch quality of textual and pictorial regions, whose feature only extracted from distorted images owns strong noise robust and effects on both FR and NR IQA; 3) Considering the characteristics of SCIs, a deep and valid network architecture is designed for both NR and FR visual quality evaluation of SCIs. Experimental results verify that our model outperforms both current no-reference and full-reference image quality assessment methods on the benchmark screen content image quality assessment database (SIQAD).

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)