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: "fully convolutional siamese networks for change detection"
  Abstract: This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.

Connected through hyperedge HE-1 (induced by source paper "a novel inspection system for variable data printing using deep learning"):
  with members:
    - "anonet weakly supervised anomaly detection in textured surfaces"
    - "slum segmentation and change detection a deep learning approach"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning"
    - "spot the difference by object detection"
    - "light weight refinenet for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "real time end to end action detection with two stream networks"

Connected through hyperedge HE-2 (induced by source paper "dasnet dual attentive fully convolutional siamese networks for change detection of high resolution satellite images"):
  with members:
    - "detecting urban changes with recurrent neural networks from multitemporal sentinel 2 data"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "attention is all you need"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "a structured self attentive sentence embedding"
    - "disan directional self attention network for rnn cnn free language understanding"

Connected through hyperedge HE-3 (induced by source paper "semantic change pattern analysis"):
  with members:
    - "robust change captioning"
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "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-4 (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"
    - "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-5 (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"
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (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"
    - "high resolution semantic change detection"
    - "dense optical flow based change detection network robust to difference of camera viewpoints"

Connected through hyperedge HE-7 (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"
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "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"

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)