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 "improving long handwritten text line recognition with convolutional multi way associative memory"):
  Abstract: Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, they are likely to suffer from vanishing/exploding gradient problems when processing long text images, which are commonly found in scanned documents. This poses a major challenge to goal of completely solving Optical Character Recognition (OCR) problem. Inspired by recently proposed memory-augmented neural networks (MANNs) for long-term sequential modeling, we present a new architecture dubbed Convolutional Multi-way Associative Memory (CMAM) to tackle the limitation of current CRNNs. By leveraging recent memory accessing mechanisms in MANNs, our architecture demonstrates superior performance against other CRNN counterparts in three real-world long text OCR datasets.

Includes members:
  Member "learning to remember more with less memorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "depth adaptive graph recurrent network for text classification")
  Member "dual control memory augmented neural networks for treatment recommendations":
    also connected through other hyperedges:
      (none)
  Member "memory augmented neural networks for predictive process analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decay replay mining to predict next process events")
  Member "robust and scalable differentiable neural computer for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to remember more with less memorization")
  Member "rosetta large scale system for text detection and recognition in images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep multi modal sets")
  Member "dual memory neural computer for asynchronous two view sequential learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self attentive associative memory")
  Member "handwritten digit string recognition by combination of residual network and rnn ctc":
    also connected through other hyperedges:
      (none)
  Member "implicit language model in lstm for ocr":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient lexicon free ocr using deep learning")

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)