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 "deep distance transform for tubular structure segmentation in ct scans"):
  Abstract: Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and complicated background. A tubular structure usually has a cylinder-like shape which can be well represented by its skeleton and cross-sectional radii (scales). Inspired by this, we propose a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks. DDT first learns a multi-task network to predict a segmentation mask for a tubular structure and a distance map. Each value in the map represents the distance from each tubular structure voxel to the tubular structure surface. Then the segmentation mask is refined by leveraging the shape prior reconstructed from the distance map. We apply our DDT on six medical image datasets. The experiments show that (1) DDT can boost tubular structure segmentation performance significantly (e.g., over 13% improvement measured by DSC for pancreatic duct segmentation), and (2) DDT additionally provides a geometrical measurement for a tubular structure, which is important for clinical diagnosis (e.g., the cross-sectional scale of a pancreatic duct can be an indicator for pancreatic cancer).

Includes members:
  Member "one network to segment them all a general lightweight system for accurate 3d medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "a large annotated medical image dataset for the development and evaluation of segmentation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the future of digital health with federated learning")
  Member "nnu net breaking the spell on successful 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 "abdominal multi organ segmentation with organ attention networks and statistical fusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "kid net convolution networks for kidney vessels segmentation from ct volumes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cascade decoder a universal decoding method for biomedical image segmentation")
  Member "deepflux for skeletons in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "textfield learning a deep direction field for irregular scene text detection")
  Member "3d semi supervised learning with uncertainty aware multi view co training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "training multi organ segmentation networks with sample selection by relaxed upper confident bound":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation")

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)