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: "joint vertebrae identification and localization in spinal ct images by combining short and long range contextual information"
  Abstract: Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the  short-range contextual information , such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important  long-range contextual information  to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "iterative fully convolutional neural networks for automatic vertebra segmentation and identification"):
  with members:
    - "semi convolutional operators for instance segmentation"
    - "a survey on deep learning in medical image analysis"
    - "fully automatic segmentation of lumbar vertebrae from ct images using cascaded 3d fully convolutional networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"
    - "a localisation segmentation approach for multi label annotation of lumbar vertebrae using deep nets"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-2 (induced by source paper "adversarially learning a local anatomical prior vertebrae labelling with 2d reformations"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "btrfly net vertebrae labelling with energy based adversarial learning of local spine prior"
    - "improved training of wasserstein gans"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

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)