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: "virtualization of tissue staining in digital pathology using an unsupervised deep learning approach"
  Abstract: Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.

Connected through hyperedge HE-1 (induced by source paper "self supervised similarity learning for digital pathology"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "deep learning is robust to massive label noise"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "unsupervised representation learning by predicting image rotations"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "cell detection in microscopy images with deep convolutional neural network and compressed sensing"
    - "colorful image colorization"
    - "deep residual learning for image recognition"

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"
    - "staingan stain style transfer for digital histological images"
    - "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"

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)