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: "ensembles of multiple models and architectures for robust brain tumour segmentation"
  Abstract: Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.

Connected through hyperedge HE-1 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs"):
  with members:
    - "patch based output space adversarial learning for joint optic disc and cup segmentation"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "why rankings of biomedical image analysis competitions should be interpreted with care"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "deep learning to assess glaucoma risk and associated features in fundus images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "harmonious attention network for person re identification"
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "compnet complementary segmentation network for brain mri extraction"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"

Connected through hyperedge HE-3 (induced by source paper "improving 3d u net for brain tumor segmentation by utilizing lesion prior"):
  with members:
    - "on the convergence of adam and beyond"
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "group normalization"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "joint learning of brain lesion and anatomy segmentation from heterogeneous datasets"):
  with members:
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "hemis hetero modal image segmentation"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "sub cortical brain structure segmentation using f cnn s"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "mmfnet a multi modality mri fusion network for segmentation of nasopharyngeal carcinoma"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "yolov3 an incremental improvement"
    - "3d roi aware u net for accurate and efficient colorectal tumor segmentation"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "cbam convolutional block attention module"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"

Connected through hyperedge HE-6 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "unbiased shape compactness for segmentation"
    - "gridnet with automatic shape prior registration for automatic mri cardiac segmentation"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"

Connected through hyperedge HE-7 (induced by source paper "attention gated networks learning to leverage salient regions in medical images"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "learn to pay attention"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "attention is all you need"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "3d fcn feature driven regression forest based pancreas localization and segmentation"

Connected through hyperedge HE-8 (induced by source paper "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "fastventricle cardiac segmentation with enet"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic segmentation and disease classification using cardiac cine mr images"
    - "2d 3d fully convolutional neural networks for cardiac mr 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)