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: "development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer"
  Abstract: For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established by genitourinary specialist pathologists. On the validation dataset, the mean accuracy among 29 general pathologists was 0.61. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

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"
    - "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"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"

Connected through hyperedge HE-2 (induced by source paper "improving prostate cancer detection with breast histopathology images"):
  with members:
    - "coupling weak and strong supervision for classification of prostate cancer histopathology images"
    - "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"
    - "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-3 (induced by source paper "reg r cnn lesion detection and grading under noisy labels"):
  with members:
    - "learning from noisy labels by regularized estimation of annotator confusion"
    - "retina u net embarrassingly simple exploitation of segmentation supervision for medical object detection"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "adam a method for stochastic optimization"

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)