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: "assessment of breast cancer histology using densely connected convolutional networks"
  Abstract: Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).

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"
    - "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-2 (induced by source paper "da refinenet a dual input whole slide image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "multi resolution networks for semantic segmentation in whole slide images"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "improving whole slide segmentation through visual context a systematic study"
    - "attention is all you need"
    - "deep learning applied to nlp"
    - "gp unet lesion detection from weak labels with a 3d regression network"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "ca refinenet a dual input wsi image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "improving whole slide segmentation through visual context a systematic study"
    - "deep learning applied to nlp"
    - "convolutional sequence to sequence learning"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image 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)