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: "classification and retrieval of digital pathology scans a new dataset"
  Abstract: In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.

Connected through hyperedge HE-1 (induced by source paper "learning with less data via weakly labeled patch classification in digital pathology"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "improving prostate cancer detection with breast histopathology images"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "rotation equivariant cnns for digital pathology"
    - "sampling matters in deep embedding learning"
    - "no fuss distance metric learning using proxies"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "automated classification of histopathology images using transfer learning"):
  with members:
    - "machine learning methods for histopathological image analysis"
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks for multi class histopathology image classification"):
  with members:
    - "machine learning methods for histopathological image analysis"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "patch transformer for multi tagging whole slide histopathology images"):
  with members:
    - "attention based deep multiple instance learning"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "machine learning methods for histopathological image analysis"):
  with members:
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "detecting cancer metastases on gigapixel pathology images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "neural stain style transfer learning using gan for histopathological images"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "automatic breast cancer grading in lymph nodes using a deep neural network"

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)