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: "a bottom up approach for automatic pancreas segmentation in abdominal ct scans"
  Abstract: Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis. For anatomically high-variability abdominal organs such as the pancreas, previous segmentation works report low accuracies when comparing to organs like the heart or liver. In this paper, a fully-automated bottom-up method is presented for pancreas segmentation, using abdominal computed tomography (CT) scans. The method is based on a hierarchical two-tiered information propagation by classifying image patches. It labels superpixels as pancreas or not via pooling patch-level confidences on 2D CT slices over-segmented by the Simple Linear Iterative Clustering approach. A supervised random forest (RF) classifier is trained on the patch level and a two-level cascade of RFs is applied at the superpixel level, coupled with multi-channel feature extraction, respectively. On six-fold cross-validation using 80 patient CT volumes, we achieved 68.8% Dice coefficient and 57.2% Jaccard Index, comparable to or slightly better than published state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction"):
  with members:
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
    - "a bottom up approach for pancreas segmentation using cascaded superpixels and deep image patch labeling"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (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"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "a bottom up approach for pancreas segmentation using cascaded superpixels and deep image patch labeling"):
  with members:
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep convolutional networks for pancreas segmentation in ct imaging"
    - "handcrafted local features are convolutional neural networks"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "feedforward semantic segmentation with zoom out features"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional networks for pancreas segmentation in ct imaging"):
  with members:
    - "one weird trick for parallelizing convolutional neural networks"
    - "detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "deeporgan multi level deep convolutional networks for automated pancreas segmentation"):
  with members:
    - "feedforward semantic segmentation with zoom out features"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"
    - "rich feature hierarchies for accurate object detection and semantic 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)