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 "da refinenet a dual input whole slide image segmentation algorithm based on attention"):
  Abstract: Due to the high resolution of pathological images, the automated semantic segmentation in the medical pathological images has shown greater challenges than that in natural images. Sliding Window method has shown its effect on solving problem caused by the high resolution of whole slide images (WSI). However, owing to its localization, Sliding Window method also suffers from lack of global information. In this paper, a dual input semantic segmentation network based on attention is proposed, in which, one input provides small-scale fine information, the other input provides large-scale coarse information. Compared with single input methods, our method based on dual inputs and attention: DA-RefineNet exhibits a dramatic performance improvement on ICIAR2018 breast cancer segmentation task.

Includes members:
  Member "learning a discriminative feature network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "multi resolution networks for semantic segmentation in whole slide images":
    also connected through other hyperedges:
      (none)
  Member "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images")
  Member "assessment of breast cancer histology using densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bach grand challenge on breast cancer histology images")
  Member "improving whole slide segmentation through visual context a systematic study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sos selective objective switch for rapid immunofluorescence whole slide image classification")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep learning applied to nlp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "gp unet lesion detection from weak labels with a 3d regression network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks")

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)