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 "weakly supervised silhouette based semantic change detection"):
  Abstract: This paper presents a novel semantic change detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this new task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into change detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the change detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we create a publicly available dataset for semantic change detection to evaluate the proposed method. Both the robustness to viewpoint difference in change detection task and the effectiveness for semantic change detection of the proposed networks are verified by the experimental results.

Includes members:
  Member "panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "robust change captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")
  Member "m4cd a robust change detection method for intelligent visual surveillance":
    also connected through other hyperedges:
      (none)
  Member "learning to measure change fully convolutional siamese metric networks for scene change detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel inspection system for variable data printing using deep learning")
  Member "learning to adapt structured output space for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "learning to describe differences between pairs of similar images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adjusting image attributes of localized regions with low level dialogue")
  Member "fully convolutional siamese networks for change detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a novel inspection system for variable data printing using deep learning")
  Member "high resolution semantic change detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guided anisotropic diffusion and iterative learning for weakly supervised change detection")

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)