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 segmentation from extreme points"):
  Abstract: Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network's predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis.

Includes members:
  Member "deepigeos a deep interactive geodesic framework for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis")
  Member "interactive segmentation of medical images through fully convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "constrained cnn losses for weakly supervised segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist")
  Member "self learning to detect and segment cysts in lung ct images without manual annotation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self learning ai framework for skin lesion image segmentation and classification")
  Member "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation")
  Member "interactive medical image segmentation using deep learning with image specific fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "learning to segment medical images with scribble supervision alone":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")

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)