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: "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
  Abstract: Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 ± 3.8 (range [79.8, 94.8])% in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.

Connected through hyperedge HE-1 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unet a nested u net architecture for medical image segmentation"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "model driven deep learning for physical layer communications"
    - "joint cs mri reconstruction and segmentation with a unified deep network"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "abdominal multi organ segmentation with organ attention networks and statistical fusion"):
  with members:
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "ron reverse connection with objectness prior networks for object detection"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "deep learning and its application to medical image segmentation"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "the importance of skip connections in biomedical image segmentation"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-4 (induced by source paper "on the influence of dice loss function in multi class organ segmentation of abdominal ct using 3d fully convolutional networks"):
  with members:
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "a 3d coarse to fine framework for volumetric medical image segmentation"):
  with members:
    - "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"
    - "brain tumor segmentation with deep neural networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "3d densely convolutional networks for volumetric segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas 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)