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: "3d mri brain tumor segmentation using autoencoder regularization"
  Abstract: Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.

Connected through hyperedge HE-1 (induced by source paper "boosting segmentation with weak supervision from image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "large scale gan training for high fidelity natural image synthesis"
    - "pathology segmentation using distributional differences to images of healthy origin"

Connected through hyperedge HE-2 (induced by source paper "diminishing the effect of adversarial perturbations via refining feature representation"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "on evaluating adversarial robustness"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "vulnerability analysis of chest x ray image classification against adversarial attacks"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (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"
    - "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"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "towards semi supervised segmentation via image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "pathology segmentation using distributional differences to images of healthy origin"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"

Connected through hyperedge HE-5 (induced by source paper "attention guided anomaly detection and localization in images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving unsupervised defect segmentation by applying structural similarity to autoencoders"
    - "anomaly detection with adversarial dual autoencoders"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "video action transformer network"
    - "large scale gan training for high fidelity natural image synthesis"
    - "tell me where to look guided attention inference network"

Connected through hyperedge HE-6 (induced by source paper "3d dilated multi fiber network for real time brain tumor segmentation in mri"):
  with members:
    - "multi fiber networks for video recognition"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "no new net"
    - "autofocus layer for semantic segmentation"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "brain tumor segmentation with deep neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "multi task attention based semi supervised learning for medical image segmentation"):
  with members:
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "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"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

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)