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 "training multi organ segmentation networks with sample selection by relaxed upper confident bound"):
  Abstract: Deep convolutional neural networks (CNNs), especially fully convolutional networks, have been widely applied to automatic medical image segmentation problems, e.g., multi-organ segmentation. Existing CNN-based segmentation methods mainly focus on looking for increasingly powerful network architectures, but pay less attention to data sampling strategies for training networks more effectively. In this paper, we present a simple but effective sample selection method for training multi-organ segmentation networks. Sample selection exhibits an exploitation-exploration strategy, i.e., exploiting hard samples and exploring less frequently visited samples. Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB). Compared with other sample selection policies, e.g., Upper Confident Bound (UCB), it exploits a range of hard samples rather than being stuck with a small set of very hard ones, which mitigates the influence of annotation errors during training. We apply this new sample selection policy to training a multi-organ segmentation network on a dataset containing 120 abdominal CT scans and show that it boosts segmentation performance significantly.

Includes members:
  Member "abdominal multi organ segmentation with organ attention networks and statistical fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "hierarchical 3d fully convolutional networks for multi organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "multi stage multi recursive input fully convolutional networks for neuronal boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "boundary aware feature propagation for scene segmentation")
  Member "spatial aggregation of holistically nested networks for automated pancreas segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction")
  Member "training region based object detectors with online hard example mining":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "3d u net learning dense volumetric segmentation from sparse annotation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional occupancy networks")
  Member "a fixed point model for pancreas segmentation in abdominal ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation")
  Member "dense volume to volume vascular boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image 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)