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: "deep convolutional neural networks for breast cancer histology image analysis"
  Abstract: Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to disagreement between pathologists. Computer-aided diagnosis systems show potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.

Connected through hyperedge HE-1 (induced by source paper "multi scale domain adversarial multiple instance cnn for cancer subtype classification with non annotated histopathological images"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "attention based deep multiple instance learning"
    - "multiple instance learning for heterogeneous images training a cnn for histopathology"
    - "xception deep learning with depthwise separable convolutions"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "bach grand challenge on breast cancer histology images"):
  with members:
    - "improve the performance of transfer learning without fine tuning using dissimilarity based multi view learning for breast cancer histology images"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "data augmentation by pairing samples for images classification"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "hierarchical resnext models for breast cancer histology image classification"
    - "context aware learning using transferable features for classification of breast cancer histology images"
    - "ensembling neural networks for digital pathology images classification and segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-3 (induced by source paper "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "data efficient image recognition with contrastive predictive coding"
    - "cluster based learning from weakly labeled bags in digital pathology"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "attention based deep multiple instance learning"
    - "classification of breast cancer histology using deep learning"
    - "smooth loss functions for deep top k classification"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "general purpose genp bioimage ensemble of handcrafted and learned features with data augmentation"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "synthetic medical images from dual generative adversarial networks"
    - "towards effective codebookless model for image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and transfer learning technique"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "look investigate and classify a deep hybrid attention method for breast cancer classification"):
  with members:
    - "recurrent models of visual attention"

Connected through hyperedge HE-7 (induced by source paper "automatic instrument segmentation in robot assisted surgery using deep learning"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "angiodysplasia detection and localization using deep convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient 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)