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 "retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection"):
  Abstract: The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the coarse localization task by predicting pixel-level scores, requiring ad-hoc heuristics when mapping back to object-level scores. State-of-the-art object detectors on the other hand, allow for individual object scoring in an end-to-end fashion, while ironically trading in the ability to exploit the full pixel-wise supervision signal. This can be particularly disadvantageous in the setting of medical image analysis, where data sets are notoriously small. In this paper, we propose Retina U-Net, a simple architecture, which naturally fuses the Retina Net one-stage detector with the U-Net architecture widely used for semantic segmentation in medical images. The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors. We evaluate the importance of full segmentation supervision on two medical data sets, provide an in-depth analysis on a series of toy experiments and show how the corresponding performance gain grows in the limit of small data sets. Retina U-Net yields strong detection performance only reached by its more complex two-staged counterparts. Our framework including all methods implemented for operation on 2D and 3D images is available at github.com/pfjaeger/medicaldetectiontoolkit.

Includes members:
  Member "hybrid task cascade for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "angiodysplasia detection and localization using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "albumentations fast and flexible image augmentations")
  Member "nnu net self adapting framework for u net based medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  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 "path aggregation network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "3d context enhanced region based convolutional neural network for end to end lesion detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "what can help pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "megdet a large mini batch object detector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")

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)