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 "analysis of multi field of view cnn and attention cnn on h e stained whole slide images on hepatocellular carcinoma"):
  Abstract: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of view can not capture the information on a cellular scale. In this work, the effect of tile size on performance for classification problem is analysed. In addition, Multi Field of View CNN is assigned for taking advantage of the information provided by different tile sizes and Attention CNN is assigned for giving the capability of voting most contributing tile size. It is found that employing more than one tile size significantly increases the performance of the classification by 3.97% and both algorithms are found successful over the algorithm which uses only one tile size.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "adaptive weighting multi field of view cnn for semantic segmentation in pathology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trained model fusion for object detection using gating network")
  Member "patch based convolutional neural network for whole slide tissue image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")

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)