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 using partially annotated data methods and annotation strategies"
  Abstract: Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "consistent estimation of the max flow problem towards unsupervised image segmentation"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised image segmentation via maximum a posteriori estimation of continuous max flow"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "employing weak annotations for medical image analysis problems"):
  with members:
    - "early experiences with crowdsourcing airway annotations in chest ct"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "learning under distributed weak supervision"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "learning under distributed weak supervision"):
  with members:
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "caffe convolutional architecture for fast feature embedding"
    - "microsoft coco common objects in context"
    - "fully convolutional networks for 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)