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: "scalable multimodal convolutional networks for brain tumour segmentation"
  Abstract: Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.

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 "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"):
  with members:
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "brain tumor segmentation on mri with missing modalities"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "multimodal unsupervised image to image translation"
    - "learning factorized multimodal representations"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "improving cnn training using disentanglement for liver lesion classification in ct"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"

Connected through hyperedge HE-3 (induced by source paper "disentangled representation learning in cardiac image analysis"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "multimodal unsupervised image to image translation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a variational u net for conditional appearance and shape generation"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "hyperdense net a hyper densely connected cnn for multi modal image segmentation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "multi scale dense networks for resource efficient image classification"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-5 (induced by source paper "interactive medical image segmentation using deep learning with image specific fine tuning"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "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"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "brain tumor segmentation with deep neural networks"
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"):
  with members:
    - "niftynet a deep learning platform for medical imaging"
    - "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"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "wide residual networks"

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)