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: "urban change detection for multispectral earth observation using convolutional neural networks"
  Abstract: The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.

Connected through hyperedge HE-1 (induced by source paper "semantic change pattern analysis"):
  with members:
    - "robust change captioning"
    - "fully convolutional siamese networks for change detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "semantic change detection with hypermaps"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "procedural synthesis of remote sensing images for robust change detection with neural networks"):
  with members:
    - "targeted change detection in remote sensing images"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "focal loss for dense object detection"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "guided anisotropic diffusion and iterative learning for weakly supervised change detection"):
  with members:
    - "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"
    - "high resolution semantic 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-4 (induced by source paper "detecting urban changes with recurrent neural networks from multitemporal sentinel 2 data"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "fully convolutional siamese networks for change detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "multitask learning for large scale semantic change detection"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "fully convolutional siamese networks for change detection"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "fully convolutional siamese networks for object tracking"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "high resolution semantic change detection"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "fully convolutional siamese networks for object tracking"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "fully convolutional siamese networks for change detection"):
  with members:
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "fully convolutional siamese networks for object tracking"
    - "u net convolutional networks for biomedical image segmentation"
    - "learning to compare image patches via convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"

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)