human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  Abstract: Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for ImageNet and 0.001MP for CIFAR-10). In some domains such as medical imaging, multi-megapixel images are needed to identify the presence of disease accurately. We propose a novel method to directly train convolutional neural networks using any input image size end-to-end. This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image. In this work, we show a proof of concept using images of up to 66-megapixels (8192x8192), saving approximately 50GB of memory per image. Using two public challenge datasets, we demonstrate that CNNs can learn to extract relevant information from these large images and benefit from increasing resolution. We improved the area under the receiver-operating characteristic curve from 0.580 (4MP) to 0.706 (66MP) for metastasis detection in breast cancer (CAMELYON17). We also obtained a Spearman correlation metric approaching state-of-the-art performance on the TUPAC16 dataset, from 0.485 (1MP) to 0.570 (16MP). Code to reproduce a subset of the experiments is available at this https URL.

Includes members:
  Member "efficient memory management for gpu based deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning")
  Member "processing megapixel images with deep attention sampling models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "predicting breast tumor proliferation from whole slide images the tupac16 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "neural image compression for gigapixel histopathology image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for whole slide image analysis an overview")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "da refinenet a dual input whole slide image segmentation algorithm based on attention")
  Member "learning to zoom a saliency based sampling layer for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")

Question: Please tell me which class the source paper 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)