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: "terabyte scale deep multiple instance learning for classification and localization in pathology"
  Abstract: In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets. Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic. Here, we have gathered a dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset. Given the size of our dataset it is possible for us to train a deep learning model under the Multiple Instance Learning (MIL) assumption where only the overall slide diagnosis is necessary for training, avoiding all the expensive pixel-wise annotations that are usually part of supervised learning approaches. We test our framework on a complex task, that of prostate cancer diagnosis on needle biopsies. We performed a thorough evaluation of the performance of our MIL pipeline under several conditions achieving an AUC of 0.98 on a held-out test set of 1,824 slides. These results open the way for training accurate diagnosis prediction models at scale, laying the foundation for decision support system deployment in the clinic.

Connected through hyperedge HE-1 (induced by source paper "an attention based multi resolution model for prostate whole slide imageclassification and localization"):
  with members:
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer"
    - "attention based deep multiple instance learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "coupling weak and strong supervision for classification of prostate cancer histopathology images"
    - "tell me where to look guided attention inference network"
    - "adversarial domain adaptation for classification of prostate histopathology whole slide images"

Connected through hyperedge HE-2 (induced by source paper "extracting 2d weak labels from volume labels using multiple instance learning in ct hemorrhage detection"):
  with members:
    - "distributed deep learning for robust multi site segmentation of ct imaging after traumatic brain injury"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "3d anisotropic hybrid network transferring convolutional features from 2d images to 3d anisotropic volumes"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "improving prostate cancer detection with breast histopathology images"):
  with members:
    - "development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer"
    - "coupling weak and strong supervision for classification of prostate cancer histopathology images"
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (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"
    - "neural image compression for gigapixel histopathology image analysis"
    - "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"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (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"
    - "virtualization of tissue staining in digital pathology using an unsupervised deep learning approach"
    - "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-6 (induced by source paper "pathologist level grading of prostate biopsies with artificial intelligence"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "segmenting potentially cancerous areas in prostate biopsies using semi automatically annotated data"):
  with members:
    - "a deep learning framework for automatic diagnosis in lung cancer"
    - "y net a deep convolutional neural network for polyp detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "towards unsupervised cancer subtyping predicting prognosis using a histologic visual dictionary"):
  with members:
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "computational pathology challenges and promises for tissue 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)