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: "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
  Abstract: Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR). However, most current DCNN-based HCCR approaches treat the handwritten sample simply as an image bitmap, ignoring some vital domain-specific information that may be useful but that cannot be learnt by traditional networks. In this paper, we propose an enhancement of the DCNN approach to online HCCR by incorporating a variety of domain-specific knowledge, including deformation, non-linear normalization, imaginary strokes, path signature, and 8-directional features. Our contribution is twofold. First, these domain-specific technologies are investigated and integrated with a DCNN to form a composite network to achieve improved performance. Second, the resulting DCNNs with diversity in their domain knowledge are combined using a hybrid serial-parallel (HSP) strategy. Consequently, we achieve a promising accuracy of 97.20% and 96.87% on CASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best results previously reported in the literature.

Connected through hyperedge HE-1 (induced by source paper "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network"):
  with members:
    - "building efficient cnn architecture for offline handwritten chinese character recognition"
    - "generative adversarial network for medical images mi gan"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "convolutional neural networks applied to house numbers digit classification"

Connected through hyperedge HE-2 (induced by source paper "a limited size ensemble of homogeneous cnn lstms for high performance word classification"):
  with members:
    - "a large scale field test on word image classification in large historical document collections using a traditional and two deep learning methods"
    - "handwriting recognition using cohort of lstm and lexicon verification with extremely large lexicon"
    - "character level incremental speech recognition with recurrent neural networks"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "constructing long short term memory based deep recurrent neural networks for large vocabulary speech recognition"
    - "adadelta an adaptive learning rate method"
    - "ocr post processing error correction algorithm using google online spelling suggestion"

Connected through hyperedge HE-3 (induced by source paper "neural architecture based on fuzzy perceptual representation for online multilingual handwriting recognition"):
  with members:
    - "fast multi language lstm based online handwriting recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "a hybrid nn hmm modeling technique for online arabic handwriting recognition"

Connected through hyperedge HE-4 (induced by source paper "skeleton based gesture recognition using several fully connected layers with path signature features and temporal transformer module"):
  with members:
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "toward high performance online hccr"
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "convolutional pose machines"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "transition forests learning discriminative temporal transitions for action recognition and detection"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"):
  with members:
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "toward high performance online hccr"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "dynamic network surgery for efficient dnns"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"

Connected through hyperedge HE-7 (induced by source paper "toward high performance online hccr"):
  with members:
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "deep residual learning for image recognition"
    - "deepwriterid an end to end online text independent writer identification system"
    - "empirical evaluation of rectified activations in convolutional network"
    - "going deeper with convolutions"
    - "fractional max pooling"

Connected through hyperedge HE-8 (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"
    - "fully convolutional recurrent network for handwritten chinese text recognition"
    - "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"

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)