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 simple and robust convolutional attention network for irregular text recognition"):
  Abstract: Reading irregular text of arbitrary shape in natural scene images is still a challenging problem. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural networks for sequence modeling. In this work, we propose a simple yet robust approach for irregular text recognition. With no need to convert input images to sequence representations, we directly connect two-dimensional CNN features to an attention-based sequence decoder. As no recurrent module is adopted, our model can be trained in parallel. It achieves 3x to 18x acceleration to backward pass and 2x to 12x acceleration to forward pass, compared with the RNN counterparts. The proposed model is trained with only word-level annotations. With this simple design, our method achieves state-of-the-art or competitive recognition performance on the evaluated regular and irregular scene text benchmark datasets. Furthermore, we show that the recognition performance does not significantly degrade with inaccurate bounding boxes. This is desirable for tasks of end-to-end text detection and recognition: robust recognition performance can still be achieved with an inaccurate text detector. We will release the code.

Includes members:
  Member "universal transformers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "single shot scene text retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features")
  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 "nrtr a no recurrence sequence to sequence model for scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bidirectional scene text recognition with a single decoder")
  Member "edit probability for scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient backbone search for scene text recognition")
  Member "scan sliding convolutional attention network for scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "2d ctc for scene text recognition")
  Member "vizwiz grand challenge answering visual questions from blind people":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "in defense of grid features for visual question answering")
  Member "show attend and read a simple and strong baseline for irregular text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient backbone search 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)