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 "dfpenet geology a deep learning framework for high precision recognition and segmentation of co seismic landslides"):
  Abstract: This paper develops a robust model, Dense Feature Pyramid with Encoder-decoder Network (DFPENet), to understand and fuse the multi-scale features of objects in remote sensing images. The proposed method achieves a competitive segmentation accuracy on the public ISPRS 2D Semantic. Furthermore, a comprehensive and widely-used scheme is proposed for co-seismic landslide recognition, which integrates image features extracted from the DFPENet model, geologic features, temporal resolution, landslide spatial analysis, and transfer learning, while only RGB images are used. To corroborate its feasibility and applicability, the proposed scheme is applied to two earthquake-triggered landslides in Jiuzhaigou (China) and Hokkaido (Japan), using available pre- and post-earthquake remote sensing images. The experiments show that the proposed scheme presents a new state-of-the-art performance in regional landslide identification, and performs well in different seismic landslide recognition tasks, though landslide boundary error is not considered. The proposed scheme demonstrates a competitive performance for high-precision, high-efficiency and cross-scene recognition of earthquake disasters, which may serve as a starting point for the application of deep learning methods in co-seismic landslide recognition.

Includes members:
  Member "representation similarity analysis for efficient task taxonomy transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "depara deep attribution graph for deep knowledge transferability")
  Member "gff gated fully fusion for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weight mechanism adding a constant in concatenation of series connect")
  Member "attention u net learning where to look for the pancreas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "pyramid attention network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "convolutional neural networks for medical image analysis full training or fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the future of digital health with federated learning")
  Member "dilated residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)