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 "character based handwritten text transcription with attention networks"):
  Abstract: The paper approaches the task of handwritten text transcription with attentional encoder-decoder networks that are trained on sequences of characters. We experiment on lines of text from a popular handwriting database and compare different attention mechanisms for the decoder. The model trained with softmax attention achieves the lowest test error, outperforming several other RNN-based models. Softmax attention is able to learn a linear alignment between image pixels and target characters whereas the alignment generated by sigmoid attention is linear but much less precise. When no function is used to obtain attention weights, the model performs poorly because it lacks a precise alignment between the source and text output.

Includes members:
  Member "recursive recurrent nets with attention modeling for ocr in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient backbone search for scene text recognition")
  Member "joint line segmentation and transcription for end to end handwritten paragraph recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decoupled attention network for text recognition")
  Member "show attend and tell neural image caption generation with visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "attention based models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient content based sparse attention with routing transformers")
  Member "dropout improves recurrent neural networks for handwriting recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory")
  Member "practical recommendations for gradient based training of deep architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)