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 pancreas segmentation using cascaded superpixels and deep image patch labeling"
  Abstract: Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as the pancreas, previous approaches report undesirably low accuracies. We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels. There are four stages: 1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; 2) computing pancreas class probability maps via dense patch labeling; 3) classifying superpixels by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and 4) simple connectivity based post-processing. The dense image patch labeling are conducted by: efficient random forest classifier on image histogram, location and texture features; and more expensive (but with better specificity) deep convolutional neural network classification on larger image windows (with more spatial contexts). Evaluation of the approach is performed on a database of 80 manually segmented CT volumes in six-fold cross-validation (CV). Our achieved results are comparable, or better than the state-of-the-art methods (evaluated by "leave-one-patient-out"), with Dice 70.7% and Jaccard 57.9%. The computational efficiency has been drastically improved in the order of 6~8 minutes, comparing with others of ~10 hours per case. Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same datasets. Under six-fold CV, our bottom-up segmentation method significantly outperforms its MALF counterpart: (70.7 +/- 13.0%) versus (52.5 +/- 20.8%) in Dice. Deep CNN patch labeling confidences offer more numerical stability, reflected by smaller standard deviations.

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"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "harmonious attention network for person re identification"
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "compnet complementary segmentation network for brain mri extraction"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"

Connected through hyperedge HE-3 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention u net learning where to look for the pancreas"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"

Connected through hyperedge HE-4 (induced by source paper "fully automated pancreas segmentation with two stage 3d convolutional neural networks"):
  with members:
    - "probability map guided bi directional recurrent unet for pancreas segmentation"
    - "attention u net learning where to look for the pancreas"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image 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"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"

Connected through hyperedge HE-5 (induced by source paper "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"):
  with members:
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "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"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "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"
    - "hierarchical convolutional deconvolutional neural networks for automatic liver and tumor segmentation"
    - "neural network based automatic liver tumor segmentation with random forest based candidate filtering"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "perceptual generative adversarial networks for small object detection"
    - "liver lesion segmentation informed by joint liver segmentation"

Connected through hyperedge HE-8 (induced by source paper "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion 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"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)