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: "neural image compression for gigapixel histopathology image analysis"
  Abstract: We present Neural Image Compression (NIC), a method to reduce the size of gigapixel images by mapping them to a compact latent space using neural networks. We show that this compression allows us to train convolutional neural networks on histopathology whole-slide images end-to-end using weak image-level labels.

Connected through hyperedge HE-1 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "processing megapixel images with deep attention sampling models"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "attention based deep multiple instance learning"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-2 (induced by source paper "deep learning for whole slide image analysis an overview"):
  with members:
    - "learning where to see a novel attention model for automated immunohistochemical scoring"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "attention based deep multiple instance learning"
    - "classification and disease localization in histopathology using only global labels a weakly supervised approach"
    - "cancer metastasis detection with neural conditional random field"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "a survey on deep learning in medical image analysis"

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)