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: "staingan stain style transfer for digital histological images"
  Abstract: Digitized Histological diagnosis is in increasing demand. However, color variations due to various factors are imposing obstacles to the diagnosis process. The problem of stain color variations is a well-defined problem with many proposed solutions. Most of these solutions are highly dependent on a reference template slide. We propose a deep-learning solution inspired by CycleGANs that is trained end-to-end, eliminating the need for an expert to pick a representative reference slide. Our approach showed superior results quantitatively and qualitatively against the state of the art methods (10% improvement visually using SSIM). We further validated our method on a clinical use-case, namely Breast Cancer tumor classification, showing 12% increase in AUC. The code will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "style transfer based image synthesis as an efficient regularization technique in deep learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "text to image synthesis using generative adversarial networks"
    - "gans for medical image analysis"
    - "random erasing data augmentation"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"

Connected through hyperedge HE-2 (induced by source paper "perceptual embedding consistency for seamless reconstruction of tilewise style transfer"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "computational histological staining and destaining of prostate core biopsy rgb images with generative adversarial neural networks"
    - "virtualization of tissue staining in digital pathology using an unsupervised deep learning approach"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks"):
  with members:
    - "gan based virtual re staining a promising solution for whole slide image analysis"
    - "fluorescence microscopy image segmentation using convolutional neural network with generative adversarial networks"
    - "three dimensional fluorescence microscopy image synthesis and segmentation"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "fluorescence image histology pattern transformation using image style transfer"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "fast gpu enabled color normalization for digital pathology"):
  with members:
    - "neural stain style transfer learning using gan for histopathological images"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "ink removal from histopathology whole slide images by combining classification detection and image generation models"):
  with members:
    - "yolov3 an incremental improvement"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "deep semi supervised generative learning for automated tumor proportion scoring on nsclc tissue needle biopsies"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "machine learning methods for histopathological image analysis"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "neural stain style transfer learning using gan for histopathological images"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)