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: "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"
  Abstract: Automatic localization and labeling of vertebra in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postoperative assessment. However, the unusual conditions of pathological cases, such as the abnormal spine curvature, bright visual imaging artifacts caused by metal implants, and the limited field of view, increase the difficulties of accurate localization. In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes. First, we deploy a deep image-to-image network (DI2IN) to initialize vertebra locations, employing the convolutional encoder-decoder architecture together with multi-level feature concatenation and deep supervision. Next, the centroid probability maps from DI2IN are iteratively evolved with the message passing schemes based on the mutual relation of vertebra centroids. Finally, the localization results are refined with sparsity regularization. The proposed method is evaluated on a public dataset of 302 spine CT volumes with various pathologies. Our method outperforms other state-of-the-art methods in terms of localization accuracy. The run time is around 3 seconds on average per case. To further boost the performance, we retrain the DI2IN on additional 1000+ 3D CT volumes from different patients. To the best of our knowledge, this is the first time more than 1000 3D CT volumes with expert annotation are adopted in experiments for the anatomic landmark detection tasks. Our experimental results show that training with such a large dataset significantly improves the performance and the overall identification rate, for the first time by our knowledge, reaches 90 %.

Connected through hyperedge HE-1 (induced by source paper "iterative fully convolutional neural networks for automatic vertebra segmentation and identification"):
  with members:
    - "joint vertebrae identification and localization in spinal ct images by combining short and long range contextual information"
    - "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"
    - "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 "accelerating deconvolution on unmodified cnn accelerators for generative adversarial networks a software approach"):
  with members:
    - "towards a uniform architecture for the efficient implementation of 2d and 3d deconvolutional neural networks on fpgas"
    - "ganax a unified mimd simd acceleration for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "triple generative adversarial nets"
    - "perceptual generative adversarial networks for small object detection"
    - "gp gan towards realistic high resolution image blending"
    - "a design methodology for efficient implementation of deconvolutional neural networks on an fpga"
    - "artgan artwork synthesis with conditional categorical gans"

Connected through hyperedge HE-3 (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"
    - "joint vertebrae identification and localization in spinal ct images by combining short and long range contextual information"
    - "improved training of wasserstein gans"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-4 (induced by source paper "joint vertebrae identification and localization in spinal ct images by combining short and long range contextual information"):
  with members:
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "deep multi task representation learning a tensor factorisation approach"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "btrfly net vertebrae labelling with energy based adversarial learning of local spine prior"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "ssd single shot multibox detector"
    - "energy based generative adversarial network"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "accurate detection of inner ears in head cts using a deep volume to volume regression network with false positive suppression and a shape based constraint"):
  with members:
    - "convnet based localization of anatomical structures in 3 d medical images"
    - "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)