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Center hyperedge (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis"):
  Abstract: Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval.

Includes members:
  Member "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised text line segmentation")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "offline signature verification by combining graph edit distance and triplet networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph based offline signature verification")
  Member "deep learning for classical japanese literature":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improve sgd training via aligning min batches")
  Member "deepdiva a highly functional python framework for reproducible experiments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trainable spectrally initializable matrix transformations in convolutional neural networks")
  Member "identifying cross depicted historical motifs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "large scale historical watermark recognition dataset and a new consistency based approach")
  Member "open evaluation tool for layout analysis of document images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised text line segmentation")
  Member "historical document image segmentation with lda initialized deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trainable spectrally initializable matrix transformations in convolutional 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)