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: "high resolution semantic change detection"
  Abstract: Change detection is one of the main problems in remote sensing, and is essential to the accurate processing and understanding of the large scale Earth observation data available through programs such as Sentinel and Landsat. Most of the recently proposed change detection methods bring deep learning to this context, but openly available change detection datasets are still very scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection. The dataset contains coregistered RGB image pairs, pixel-wise change information and land cover information. We then propose several methods using fully convolutional neural networks to perform semantic change detection. Most notably, we present a network architecture that performs change detection and land cover mapping simultaneously, while using the predicted land cover information to help to predict changes. We also describe a sequential training scheme that allows this network to be trained without setting a hyperparameter that balances different loss functions and achieves the best overall results.

Connected through hyperedge HE-1 (induced by source paper "guided anisotropic diffusion and iterative learning for weakly supervised change detection"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "deep learning is robust to massive label noise"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "fully convolutional siamese networks for change detection"
    - "learning from weak and noisy labels for semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised silhouette based semantic change detection"):
  with members:
    - "panoptic segmentation"
    - "robust change captioning"
    - "m4cd a robust change detection method for intelligent visual surveillance"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning to describe differences between pairs of similar images"
    - "fully convolutional siamese networks for change detection"
    - "dense optical flow based change detection network robust to difference of camera viewpoints"

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)