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: "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
  Abstract: In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from small sets of training data, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating ($r \ge 0.97$) also with the expected lesion volume.

Connected through hyperedge HE-1 (induced by source paper "unpaired multi modal segmentation via knowledge distillation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "soft labeling by distilling anatomical knowledge for improved ms lesion segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "group normalization"
    - "self supervised knowledge distillation using singular value decomposition"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"

Connected through hyperedge HE-2 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

Connected through hyperedge HE-3 (induced by source paper "deep q learning driven ct pancreas segmentation with geometry aware u net"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "detection aided liver lesion segmentation using deep learning"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deformable convolutional networks"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "tree structured reinforcement learning for sequential object localization"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "visualizing convolutional networks for mri based diagnosis of alzheimer s disease"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-5 (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"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "brain tumor segmentation with deep neural networks"
    - "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-6 (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"
    - "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"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "simultaneous lesion and neuroanatomy segmentation in multiple sclerosis using deep neural networks"):
  with members:
    - "one shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "focal loss for dense object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "sgdr stochastic gradient descent with restarts"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "multiple sclerosis lesion synthesis in mri using an encoder decoder u net"):
  with members:
    - "asymmetric loss functions and deep densely connected networks for highly imbalanced medical image segmentation application to multiple sclerosis lesion detection"
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"
    - "the importance of skip connections in biomedical image segmentation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image 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)