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: "depth agmnet an atrous granular multiscale stereo network based on depth edge auxiliary task"
  Abstract: Recently, end-to-end convolutional neural networks have achieved remarkable success in stereo estimation tasks. It is still a valuable but hard problem to find the correct correspondence for the ill-posed regions, such as texture-less areas, edge details, and small objects. Based on the above questions, this paper proposes Dedge-AGMNet to alleviate it in different methods. First, we present the multi-task network composes of a depth-edge auxiliary network and disparity estimation this http URL fuse the information of the ancillary system effectively, we design Dedge-SPP to embed the depth edge contours. Besides, On the supervised and unsupervised tasks of the depth edge, the corresponding loss function is constructed for depth edge constraints. Second, we design the AGM module, which utilized the fusion of internal fine-grained feature and stacking convolution layers within a single layer, combining with the dilated convolution and parallel structure reasonably. It is beneficial to provide dense -scale receptive fields and potential correspondence. The results show that for the unsupervised task of depth edge, the network can optimize the edge effectively. Our proposed approach shows accurate disparity estimates and achieves state-of-the-art performances on Sceneflow, KITTI2012, and KITTI2015 benchmarks datasets.

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)