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: "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
  Abstract: Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation. Under a reasonable transformation function, our approach can be factorized into two stages, and each stage can be efficiently optimized via gradient back-propagation throughout the deep networks. We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation. Without human assistance, our approach reports a 63.44% average accuracy, measured by the Dice-S{\o}rensen coefficient (DSC), which is higher than the number (60.46%) without deep supervision.

Connected through hyperedge HE-1 (induced by source paper "phase collaborative network for two phase medical image segmentation"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "deep sequential segmentation of organs in volumetric medical scans"):
  with members:
    - "densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation"
    - "lstm a search space odyssey"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "extracting 3d vascular structures from microscopy images using convolutional recurrent networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "3 d convolutional neural networks for glioblastoma segmentation"

Connected through hyperedge HE-3 (induced by source paper "lung and pancreatic tumor characterization in the deep learning era novel supervised and unsupervised learning approaches"):
  with members:
    - "s4nd single shot single scale lung nodule detection"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-4 (induced by source paper "fusionnet incorporating shape and texture for abnormality detection in 3d abdominal ct scans"):
  with members:
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "joint shape representation and classification for detecting pdac"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-5 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "interpretable structure evolving lstm"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"
    - "semantic instance segmentation via deep metric learning"

Connected through hyperedge HE-6 (induced by source paper "splenomegaly segmentation on multi modal mri using deep convolutional networks"):
  with members:
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "semi supervised multi organ segmentation via deep multi planar co training"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "deep co training for semi supervised image recognition"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "data distillation towards omni supervised learning"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"

Connected through hyperedge HE-8 (induced by source paper "phase collaborative network for multi phase medical imaging segmentation"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "clockwork convnets for video semantic segmentation"
    - "image to image translation with conditional adversarial networks"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"

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)