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 learning and its application to medical image segmentation"
  Abstract: One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows state-of-the-art performance in multi-organ segmentation.

Connected through hyperedge HE-1 (induced by source paper "addressing the memory bottleneck in ai model training"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "globally guided progressive fusion network for 3d pancreas segmentation"):
  with members:
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "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"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"):
  with members:
    - "why rankings of biomedical image analysis competitions should be interpreted with care"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"

Connected through hyperedge HE-4 (induced by source paper "computational anatomy for multi organ analysis in medical imaging a review"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "a generic approach to lung field segmentation from chest radiographs using deep space and shape learning"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "towards automatic abdominal multi organ segmentation in dual energy ct using cascaded 3d fully convolutional network"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "probably unknown deep inverse sensor modelling in radar"):
  with members:
    - "recent progress in semantic image segmentation"
    - "polar transformer networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "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)