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: "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
  Abstract: The Dice score is widely used for binary segmentation due to its robustness to class imbalance. Soft generalisations of the Dice score allow it to be used as a loss function for training convolutional neural networks (CNN). Although CNNs trained using mean-class Dice score achieve state-of-the-art results on multi-class segmentation, this loss function does neither take advantage of inter-class relationships nor multi-scale information. We argue that an improved loss function should balance misclassifications to favour predictions that are semantically meaningful. This paper investigates these issues in the context of multi-class brain tumour segmentation. Our contribution is threefold. (1) We propose a semantically-informed generalisation of the Dice score for multi-class segmentation based on the Wasserstein distance on the probabilistic label space. (2) We propose a holistic CNN that embeds spatial information at multiple scales with deep supervision. (3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice score achieves segmentations that are more semantically meaningful for brain tumour segmentation.

Connected through hyperedge HE-1 (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"
    - "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"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

Connected through hyperedge HE-2 (induced by source paper "eyenet attention based convolutional encoder decoder network for eye region segmentation"):
  with members:
    - "openeds open eye dataset"
    - "skeletonnet shape pixel to skeleton pixel"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "cbam convolutional block attention module"
    - "eye tracking for everyone"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "weighted point cloud augmentation for neural network training data class imbalance"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "splatnet sparse lattice networks for point cloud processing"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "focal loss for dense object detection"
    - "imbalanced malware images classification a cnn based approach"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "niftynet a deep learning platform for medical imaging"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "a self aware sampling scheme to efficiently train fully convolutional networks for semantic segmentation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "niftynet a deep learning platform for medical imaging"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "a volumetric convolutional neural network for brain tumor segmentation"):
  with members:
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "multimodal mri brain tumor segmentation using random forests with features learned from fully convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "hierarchical multi class segmentation of glioma images using networks with multi level activation function"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "multi level activation for segmentation of hierarchically nested classes"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "deep learning markov random field for semantic segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "ensemble of multi sized fcns to improve white matter lesion segmentation"):
  with members:
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "the importance of skip connections in biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "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)