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 end to end deep learning histochemical scoring system for breast cancer tma"):
  Abstract: One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.

Includes members:
  Member "framing u net via deep convolutional framelets application to sparse view ct":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound")
  Member "deep label distribution learning with label ambiguity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "detecting cancer metastases on gigapixel pathology images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai")
  Member "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spgnet semantic prediction guidance for scene parsing")
  Member "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lgan lung segmentation in ct scans using generative adversarial network")
  Member "deep learning assessment of tumor proliferation in breast cancer histological images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine learning methods for histopathological image analysis")
  Member "deep learning for multi task medical image segmentation in multiple modalities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "dcan deep contour aware networks for accurate gland segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion 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)