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 "fast densenet towards efficient and accurate text recognition with fast dense networks"):
  Abstract: Convolutional Recurrent Neural Network (CRNN) is a popular network for recognizing texts in images. Advances like the variants of CRNN, such as Dense Convolutional Network with Connectionist Temporal Classification, has reduced the running time of the networks, but exposing the inner computation cost of the convolutional networks as a bottleneck. Specifically, DenseNet based frameworks use the dense blocks as the core module, but the inner features are combined in the form of concatenation in dense blocks. As a result, the number of channels of combined features delivered as the input of the layers close to the output and the relevant computational cost grows rapidly with the dense blocks getting deeper. This will severely bring heavy computational cost and restrict the depth of dense blocks. In this paper, we propose an efficient convolutional block called Fast Dense Block (FDB). To reduce the computing cost, we redefine and design the way of combining internal features of dense blocks. FDB is a convolutional block similarly as the dense block, but it applies both sum and concatenating operations to connect the inner features in blocks, which can reduce the computation cost to (1/L, 2/L), compared with the original dense block, where L is the number of layers in the dense block. Importantly, since the parameters of standard dense block and our new FDB keep consistent except the way of combining features, and their inputs and outputs have the same size and same number of channels, so FDB can be easily used to replace the original dense block in any DenseNet based framework. Based on the designed FDBs, we further propose a fast network of DenseNet to improve the text recognition performance in images.

Includes members:
  Member "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully convolutional intensive feature flow neural network for text recognition")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "scene text recognition from two dimensional perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient backbone search for scene text recognition")
  Member "densereg fully convolutional dense shape regression in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes")
  Member "residual dense network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "building fast and compact convolutional neural networks for offline handwritten chinese character recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network")
  Member "aon towards arbitrarily oriented text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient backbone search for scene text recognition")
  Member "reading scene text with attention convolutional sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "faclstm convlstm with focused attention for scene text recognition")

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)