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: "cloud net a cloud segmentation cnn for landsat 8 remote sensing imagery optimized with filtered jaccard loss function"
  Abstract: Cloud Segmentation is one of the fundamental steps in optical remote sensing image analysis. Current methods for identification of cloud regions in aerial or satellite images are not accurate enough especially in the presence of snow and haze. This paper presents a deep learning-based framework to address the problem of cloud detection in Landsat 8 imagery. The proposed method benefits from a convolutional neural network (Cloud-Net+) with multiple blocks, which is trained with a novel loss function (Filtered Jaccard loss). The proposed loss function is more sensitive to the absence of cloud pixels in an image and penalizes/rewards the predicted mask more accurately. The combination of Cloud-Net+ and Filtered Jaccard loss function delivers superior results over four public cloud detection datasets. Our experiments on one of the most common public datasets in computer vision (Pascal VOC dataset) show that the proposed network/loss function could be used in other segmentation tasks for more accurate performance/evaluation.

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)