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: "ensembling neural networks for digital pathology images classification and segmentation"
  Abstract: In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images. To validate the developed approaches, we conducted experiments with \textit{Breast Cancer Histology Challenge} dataset and obtained 90\% accuracy for the 4-class tissue classification task.

Connected through hyperedge HE-1 (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"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "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"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-2 (induced by source paper "a new ensemble learning framework for 3d biomedical image segmentation"):
  with members:
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "xgboost a scalable tree boosting system"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "densely connected convolutional 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)