human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "fully convolutional recurrent network for handwritten chinese text recognition"
  Abstract: This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract signature features from the input pen-tip trajectory, a fully convolutional network to learn informative representation, a sequence modeling layer to make per-frame predictions on the input sequence and a transcription layer to translate the predictions into a label sequence. The FCRN is end-to-end trainable in contrast to conventional methods whose components are separately trained and tuned. We also present a refined beam search method that efficiently integrates the language model to decode the FCRN and significantly improve the recognition results. #R##N#We evaluate the performance of the proposed method on the test sets from the databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition competition, and both achieve state-of-the-art performance with correct rates of 96.40% and 95.00%, respectively.

Connected through hyperedge HE-1 (induced by source paper "fully convolutional networks for handwriting recognition"):
  with members:
    - "deep learning based isolated arabic scene character recognition"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "robust scene text recognition with automatic rectification"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "developing the path signature methodology and its application to landmark based human action recognition"):
  with members:
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "rmpe regional multi person pose estimation"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"

Connected through hyperedge HE-3 (induced by source paper "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"):
  with members:
    - "identity mappings in deep residual networks"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "jointly modeling embedding and translation to bridge video and language"
    - "sequence to sequence learning for optical character recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "reading scene text in deep convolutional sequences"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"

Question: Please tell me which class the center node 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)