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 multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
  Abstract: Abstract   Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel disease. Quantification of lacunes may be of great importance to elucidate the mechanisms behind neuro-degenerative disorders and is recommended as part of study standards for small vessel disease research. However, due to the different appearance of lacunes in various brain regions and the existence of other similar-looking structures, such as perivascular spaces, manual annotation is a difficult, elaborative and subjective task, which can potentially be greatly improved by reliable and consistent computer-aided detection (CAD) routines.  In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN). We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.

Connected through hyperedge HE-1 (induced by source paper "sos selective objective switch for rapid immunofluorescence whole slide image classification"):
  with members:
    - "attentive single tasking of multiple tasks"
    - "improving whole slide segmentation through visual context a systematic study"
    - "on calibration of modern neural networks"
    - "an analysis of scale invariance in object detection snip"
    - "a survey on deep learning in medical image analysis"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "deep residual learning for image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "multi branch convolutional neural network for multiple sclerosis lesion segmentation"):
  with members:
    - "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"
    - "deepvesselnet vessel segmentation centerline prediction and bifurcation detection in 3 d angiographic volumes"
    - "brain tumor segmentation with deep neural networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"

Connected through hyperedge HE-3 (induced by source paper "medical image analysis using convolutional neural networks a review"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "brain tumor segmentation with deep neural networks"
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "medical image retrieval using deep convolutional neural network"
    - "geometric convolutional neural network for analyzing surface based neuroimaging data"

Connected through hyperedge HE-4 (induced by source paper "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"):
  with members:
    - "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"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"):
  with members:
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "adversarial training and dilated convolutions for brain mri segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"
    - "semantic segmentation using adversarial networks"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "dense pooling layers in fully convolutional network for skin lesion segmentation"):
  with members:
    - "extraction of skin lesions from non dermoscopic images for surgical excision of melanoma"
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning in neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

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)