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: "learning to measure change fully convolutional siamese metric networks for scene change detection"
  Abstract: A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled. Following the intuitive idea of detecting changes by directly comparing dissimilarities between a pair of features, we propose a novel fully Convolutional siamese metric Network(CosimNet) to measure changes by customizing implicit metrics. To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pairs and to enlarge the distance between the changed feature pairs. Specifically, to address the issue of large viewpoint differences, we propose Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish noisy changes. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our approach is robust to lots of challenging conditions, such as illumination changes, large viewpoint difference caused by camera motion and zooming. In addition, we incorporate the distance metric into the segmentation framework and validate the effectiveness through visualization of change maps and feature distribution. The source code is available at this https URL.

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"
    - "detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning"
    - "spot the difference by object detection"
    - "fully convolutional siamese networks for change 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"
    - "fully convolutional siamese networks for 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 "guided anisotropic diffusion and iterative learning for weakly supervised change detection"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "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 "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 adapt structured output space for semantic segmentation"
    - "learning to describe differences between pairs of similar images"
    - "fully convolutional siamese networks for change detection"
    - "high resolution semantic 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)