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: "data augmentation using learned transformations for one shot medical image segmentation"
  Abstract: Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling medical images requires significant expertise and time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. #R##N#We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our method requires only a single segmented scan, and leverages other unlabeled scans in a semi-supervised approach. We learn a model of transformations from the images, and use the model along with the labeled example to synthesize additional labeled examples. Each transformation is comprised of a spatial deformation field and an intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. We show that training a supervised segmenter with these new examples provides significant improvements over state-of-the-art methods for one-shot biomedical image segmentation. Our code is available at this https URL.

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"
    - "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"
    - "neural multi atlas label fusion application to cardiac mr images"

Connected through hyperedge HE-2 (induced by source paper "semi supervised few shot learning for medical image segmentation"):
  with members:
    - "on the texture bias for few shot cnn segmentation"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "boosting few shot visual learning with self supervision"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"

Connected through hyperedge HE-3 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "synthetic data augmentation using gan for improved liver lesion classification"

Connected through hyperedge HE-4 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "annotation cost minimization for medical image segmentation using suggestive mixed supervision fully convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a kings ransom for encryption ransomware classification using augmented one shot learning and bayesian approximation"):
  with members:
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bayesian layers a module for neural network uncertainty"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "one shot learning with memory augmented neural networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "vm net mesh modeling to assist segmentation in volumetric data"):
  with members:
    - "neuralsampler euclidean point cloud auto encoder and sampler"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "deep sequential segmentation of organs in volumetric medical scans"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-7 (induced by source paper "few labeled atlases are necessary for deep learning based segmentation"):
  with members:
    - "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"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-8 (induced by source paper "squeeze excite guided few shot segmentation of volumetric images"):
  with members:
    - "few shot segmentation propagation with guided networks"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "one shot learning for semantic segmentation"
    - "learning feed forward one shot learners"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "one shot video object segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "matching networks for one shot learning"

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)