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 "deeply self supervising edge to contour neural network applied to liver segmentation"):
  Abstract: Accurate segmentation of liver is still a challenging problem due to its large shape variability and unclear boundaries. The purpose of this paper is to propose a neural network based liver segmentation algorithm and evaluate its performance on abdominal CT images. First, we develop a fully convolutional network (FCN) for volumetric image segmentation problem. To guide a neural network to accurately delineate the target liver object, we apply self-supervising scheme with respect to edge and contour responses. The deeply supervising method is also applied to our low-level features for further combining discriminative features in the higher feature dimensions. We used 160 abdominal CT images for training and validation. Quantitative evaluation of our proposed network is presented with 8-fold cross-validation. The result showed that our method successfully segmented liver more accurately than any other state-of-the-art methods without expanding or deepening the neural network. The proposed approach can be easily extended to other imaging protocols (e.g., MRI) or other target organ segmentation problems without any modifications of the framework.

Includes members:
  Member "stacked deconvolutional network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deeperlab single shot image parser")
  Member "improved regularization of convolutional neural networks with cutout":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "3d u net learning dense volumetric segmentation from sparse annotation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional occupancy networks")
  Member "v net fully convolutional neural networks for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)