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: "automatic image defect diagnosis"
  Abstract: Although problems relating to specific image correction have been explored intensively, the problem of simultaneous diagnosis for multiple photographic defects remains relatively untouched. Solutions to this problem attempt to predict the existence, severity, and locations of common defects. This paper proposes a first attempt at a solution to the general defect diagnosis problem based on our novel dataset. We formulate the defect diagnosis problem as a multi-task prediction problem and utilize multi-column deep neural networks (DNN) to approach the problem. We propose DNN models with holistic and multi-patch inputs and combine their predicted scores to integrate multi-scale information. During experiments, we validate the complementarity of both kinds of inputs. We also validate that our combined predictions have a more consistent ranking correlation with our ground truth than the average of individual users' judgments. Furthermore, we apply the fully convolutional version of our trained model to visualize defect severity heat maps, which can effectively identify defective regions of input images. We propose that our work will provide casual photographers with better experiences when using image editing software to improve image quality. Another promising avenue for future application involves the equipping of photo summarization systems with defect cues to focus more on defect-free photos.

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)