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 "field of interest prediction for computer aided mitotic count"):
  Abstract: Manual counts of mitotic figures, which are determined in the tumor region with the highest mitotic activity, are a key parameter of most tumor grading schemes. It is however strongly dependent on the area selection. To reduce potential variability of prognosis due to this, we propose to use an algorithmic field of interest prediction to assess the area of highest mitotic activity in a whole-slide image. Methods: We evaluated two state-of-the-art methods, all based on the use of deep convolutional neural networks on their ability to predict the mitotic count in digital histopathology slides. We evaluated them on a novel dataset of 32 completely annotated whole slide images from canine cutaneous mast cell tumors (CMCT) and one publicly available human mamma carcinoma (HMC) dataset. We first compared the mitotic counts (MC) predicted by the two models with the ground truth MC on both data sets. Second, for the CMCT data set, we compared the computationally predicted position and MC of the area of highest mitotic activity with size-equivalent areas selected by eight veterinary pathologists. Results: We found a high correlation between the mitotic count as predicted by the models (Pearson's correlation coefficient between 0.931 and 0.962 for the CMCT data set and between 0.801 and 0.986 for the HMC data set) on the slides. For the CMCT data set, this is also reflected in the predicted position representing mitotic counts in mostly the upper quartile of the slide's ground truth MC distribution. Further, we found strong differences between experts in position selection. Conclusion: While the mitotic counts in areas selected by the experts substantially varied, both algorithmic approaches were consistently able to generate a good estimate of the area of highest mitotic count. To achieve better inter-rater agreement, we propose to use computer-based area selection for manual mitotic count.

Includes members:
  Member "predicting breast tumor proliferation from whole slide images the tupac16 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images")
  Member "sliderunner a tool for massive cell annotations in whole slide images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning new tricks from old dogs inter species inter tissue domain adaptation for mitotic figure assessment")
  Member "a unified framework for tumor proliferation score prediction in breast histopathology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep object detection based mitosis analysis in breast cancer histopathological images")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning deep features for discriminative localization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "assessment of algorithms for mitosis detection in breast cancer histopathology images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "roto translation equivariant convolutional networks application to histopathology image analysis")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)