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 "guided anisotropic diffusion and iterative learning for weakly supervised change detection"):
  Abstract: Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these datasets are easier to acquire, the data are frequently noisy and unreliable, which is motivating research on weakly supervised learning techniques. In this paper we propose an iterative learning method that extracts the useful information from a large scale change detection dataset generated from open vector data to train a fully convolutional network which surpasses the performance obtained by naive supervised learning. We also propose the guided anisotropic diffusion algorithm, which improves semantic segmentation results using the input images as guides to perform edge preserving filtering, and is used in conjunction with the iterative training method to improve results.

Includes members:
  Member "urban change detection for multispectral earth observation using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic change pattern analysis")
  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 "a novel inspection system for variable data printing using deep learning")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "derivative manipulation for general example weighting")
  Member "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "fully convolutional siamese networks for change detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "weakly supervised silhouette based semantic change detection")
  Member "learning from weak and noisy labels for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives")
  Member "simple does it weakly supervised instance and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")

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)