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: "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
  Abstract: The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. #R##N#In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with hand-crafted features as well as CNNs that do not integrate location information. On a test set of 46 scans, the best configuration of our networks obtained a Dice score of 0.791, compared to 0.797 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value=0.17).

Connected through hyperedge HE-1 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "combo loss handling input and output imbalance in multi organ segmentation"
    - "organ at risk segmentation in head and neck ct images by using a two stage segmentation framework based on 3d u net"
    - "attention u net learning where to look for the pancreas"
    - "ischemic stroke lesion segmentation in ct perfusion scans using pyramid pooling and focal loss"
    - "image segmentation algorithms overview"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"

Connected through hyperedge HE-2 (induced by source paper "accurate automatic segmentation of amygdala subnuclei and modeling of uncertainty via bayesian fully convolutional neural network"):
  with members:
    - "niftynet a deep learning platform for medical imaging"
    - "dilated residual networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep retinal image understanding"

Connected through hyperedge HE-4 (induced by source paper "path capsule networks"):
  with members:
    - "dynamic routing between capsules"
    - "aggregated residual transformations for deep neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "multi path convolutional neural networks for complex image classification"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-5 (induced by source paper "measuring the quality of explanations the system causability scale scs comparing human and machine explanations"):
  with members:
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "a survey on deep learning in medical image analysis"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"):
  with members:
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "identity mappings in deep residual networks"
    - "hemis hetero modal image segmentation"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-7 (induced by source paper "quantitative analysis of patch based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging"):
  with members:
    - "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"
    - "automated sub cortical brain structure segmentation combining spatial and deep convolutional features"
    - "a survey on deep learning in medical image analysis"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-8 (induced by source paper "automated sub cortical brain structure segmentation combining spatial and deep convolutional features"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "sub cortical brain structure segmentation using f cnn s"
    - "deep residual learning for image recognition"
    - "deep neural networks for anatomical brain 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)