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 "an attention based multi resolution model for prostate whole slide imageclassification and localization"):
  Abstract: Histology review is often used as the `gold standard' for disease diagnosis. Computer aided diagnosis tools can potentially help improve current pathology workflows by reducing examination time and interobserver variability. Previous work in cancer grading has focused mainly on classifying pre-defined regions of interest (ROIs), or relied on large amounts of fine-grained labels. In this paper, we propose a two-stage attention-based multiple instance learning model for slide-level cancer grading and weakly-supervised ROI detection and demonstrate its use in prostate cancer. Compared with existing Gleason classification models, our model goes a step further by utilizing visualized saliency maps to select informative tiles for fine-grained grade classification. The model was primarily developed on a large-scale whole slide dataset consisting of 3,521 prostate biopsy slides with only slide-level labels from 718 patients. The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85.11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.

Includes members:
  Member "knowledge driven encode retrieve paraphrase for medical image report generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clara clinical report auto completion")
  Member "development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving prostate cancer detection with breast histopathology images")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "adversarial complementary learning for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable deep classification models for domain generalization")
  Member "attention gated networks learning to leverage salient regions in medical images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "coupling weak and strong supervision for classification of prostate cancer histopathology images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving prostate cancer detection with breast histopathology images")
  Member "tell me where to look guided attention inference network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explainable deep classification models for domain generalization")
  Member "adversarial domain adaptation for classification of prostate histopathology whole slide images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constrained domain adaptation for segmentation")

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)