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: "a localisation segmentation approach for multi label annotation of lumbar vertebrae using deep nets"
  Abstract: Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we propose a two-staged approach that, given a computed tomography dataset of the spine, segments the five lumbar vertebrae and simultaneously labels them. The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context. The second stage, comprised of a fully-convolutional deep network, exploits the local context in the localised lumbar region to segment and label the lumbar vertebrae in one go. Aided with practical data augmentation for training, our approach is highly generalisable, capable of successfully segmenting both healthy and abnormal vertebrae (fractured and scoliotic spines). We consistently achieve an average Dice coefficient of over 90 percent on a publicly available dataset of the xVertSeg segmentation challenge of MICCAI 2016. This is particularly noteworthy because the xVertSeg dataset is beset with severe deformities in the form of vertebral fractures and scoliosis.

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"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-2 (induced by source paper "deepvesselnet vessel segmentation centerline prediction and bifurcation detection in 3 d angiographic volumes"):
  with members:
    - "multi level activation for segmentation of hierarchically nested classes"
    - "deep retinal image understanding"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "holistically nested edge detection"

Connected through hyperedge HE-3 (induced by source paper "extreme augmentation can deep learning based medical image segmentation be trained using a single manually delineated scan"):
  with members:
    - "road extraction by deep residual u net"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "mask r cnn"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-4 (induced by source paper "fully automatic segmentation of lumbar vertebrae from ct images using cascaded 3d fully convolutional networks"):
  with members:
    - "unitbox an advanced object detection network"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "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)