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 "signet ring cell detection with a semi supervised learning framework"):
  Abstract: Signet ring cell carcinoma is a type of rare adenocarcinoma with poor prognosis. Early detection leads to huge improvement of patients' survival rate. However, pathologists can only visually detect signet ring cells under the microscope. This procedure is not only laborious but also prone to omission. An automatic and accurate signet ring cell detection solution is thus important but has not been investigated before. In this paper, we take the first step to present a semi-supervised learning framework for the signet ring cell detection problem. Self-training is proposed to deal with the challenge of incomplete annotations, and cooperative-training is adapted to explore the unlabeled regions. Combining the two techniques, our semi-supervised learning framework can make better use of both labeled and unlabeled data. Experiments on large real clinical data demonstrate the effectiveness of our design. Our framework achieves accurate signet ring cell detection and can be readily applied in the clinical trails. The dataset will be released soon to facilitate the development of the area.

Includes members:
  Member "pseudo mask augmented object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic graph convolutional networks for 3d human pose regression")
  Member "deep layer aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "learning to segment every thing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "deepcut object segmentation from bounding box annotations using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "simple does it weakly supervised instance and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "dcan deep contour aware networks for accurate gland segmentation":
    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 "weakly supervised semantic labelling and instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "environment reconstruction on depth images using generative adversarial networks")
  Member "weakly and semi supervised learning of a dcnn for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification")

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)