human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "liver segmentation in abdominal ct images via auto context neural network and self supervised contour attention"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "brain tumor segmentation with deep neural networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Question: Please tell me which class the center node 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)