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: "multi atlas segmentation of biomedical images a survey"
  Abstract: Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 - 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

Connected through hyperedge HE-1 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "one shot neural architecture search via self evaluated template network"
    - "fully convolutional one shot object segmentation for industrial robotics"
    - "learning correspondence from the cycle consistency of time"
    - "votenet a deep learning label fusion method for multi atlas segmentation"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "one shot neural architecture search through a posteriori distribution guided sampling"

Connected through hyperedge HE-2 (induced by source paper "data augmentation using learned transformations for one shot medical image segmentation"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "nonparametric density flows for mri intensity normalisation"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "few shot segmentation propagation with guided networks"
    - "autoaugment learning augmentation policies from data"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "error corrective boosting for learning fully convolutional networks with limited data"

Connected through hyperedge HE-3 (induced by source paper "abdominal multi organ segmentation with organ attention networks and statistical fusion"):
  with members:
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "few labeled atlases are necessary for deep learning based segmentation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "learning a probabilistic model for diffeomorphic registration"
    - "semi supervised and task driven data augmentation"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"

Connected through hyperedge HE-5 (induced by source paper "probabilistic atlases to enforce topological constraints"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "efficient video object segmentation via network modulation"
    - "improving cytoarchitectonic segmentation of human brain areas with self supervised siamese networks"
    - "parcellation of visual cortex on high resolution histological brain sections using convolutional neural networks"
    - "residual attention network for image classification"

Connected through hyperedge HE-6 (induced by source paper "interactive segmentation of medical images through fully convolutional neural networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "interactive full image segmentation by considering all regions jointly"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "deep extreme cut from extreme points to object segmentation"
    - "dltk state of the art reference implementations for deep learning on medical images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "3d whole brain segmentation using spatially localized atlas network tiles"):
  with members:
    - "compnet complementary segmentation network for brain mri extraction"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "4d multi atlas label fusion using longitudinal images"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-8 (induced by source paper "votenet an improved deep learning label fusion method for multi atlas segmentation"):
  with members:
    - "votenet a deep learning label fusion method for multi atlas segmentation"
    - "a novel focal tversky loss function with improved attention u net for lesion segmentation"
    - "attention u net learning where to look for the pancreas"
    - "on calibration of modern neural networks"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

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)