human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a novel inspection system for variable data printing using deep learning"):
  Abstract: We present a novel approach for inspecting variable data prints (VDP) with an ultra-low false alarm rate (0.005%) and potential applicability to other real-world problems. The system is based on a comparison between two images: a reference image and an image captured by low-cost scanners. The comparison task is challenging as low-cost imaging systems create artifacts that may erroneously be classified as true (genuine) defects. To address this challenge we introduce two new fusion methods, for change detection applications, which are both fast and efficient. The first is an early fusion method that combines the two input images into a single pseudo-color image. The second, called Change-Detection Single Shot Detector (CD-SSD) leverages the SSD by fusing features in the middle of the network. We demonstrate the effectiveness of the proposed deep learning-based approach with a large dataset from real-world printing scenarios. Finally, we evaluate our models on a different domain of aerial imagery change detection (AICD). Our best method clearly outperforms the state-of-the-art baseline on this dataset.

Includes members:
  Member "anonet weakly supervised anomaly detection in textured surfaces":
    also connected through other hyperedges:
      (none)
  Member "slum segmentation and change detection a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building change detection for remote sensing images using a dual task constrained deep siamese convolutional network model")
  Member "learning to measure change fully convolutional siamese metric networks for scene change detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dasnet dual attentive fully convolutional siamese networks for change detection of high resolution satellite images")
  Member "detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning":
    also connected through other hyperedges:
      (none)
  Member "spot the difference by object detection":
    also connected through other hyperedges:
      (none)
  Member "fully convolutional siamese networks for change detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dasnet dual attentive fully convolutional siamese networks for change detection of high resolution satellite images")
  Member "light weight refinenet for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nas count counting by density with neural architecture search")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")

Question: Please tell me which class the source paper 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)