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: "toward high performance online hccr"
  Abstract: A novel training strategy, namely DropDistortion, is proposed to improve the online handwritten Chinese character recognition.The path signature enables effective feature extraction from online characters and greatly improves the performance.Spatial stochastic max-pooling performs feature map distortion and model averaging in an effcient way. This paper presents an investigation of several techniques that increase the accuracy of online handwritten Chinese character recognition (HCCR). We propose a new training strategy named DropDistortion to train a deep convolutional neural network (DCNN) with distorted samples. DropDistortion gradually lowers the degree of character distortion during training, which allows the DCNN to better generalize. Path signature is used to extract effective features for online characters. Further improvement is achieved by employing spatial stochastic max-pooling as a method of feature map distortion and model averaging. Experiments were carried out on three publicly available datasets, namely CASIA-OLHWDB 1.0, CASIA-OLHWDB 1.1, and the ICDAR2013 online HCCR competition dataset. The proposed techniques yield state-of-the-art recognition accuracies of 97.67%, 97.30%, and 97.99%, respectively.

Connected through hyperedge HE-1 (induced by source paper "fingertip detection and tracking for recognition of air writing in videos"):
  with members:
    - "analysis of hand segmentation in the wild"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "emnist an extension of mnist to handwritten letters"
    - "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"

Connected through hyperedge HE-2 (induced by source paper "offline writer identification based on the path signature feature"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "leveraging the path signature for skeleton based human action recognition"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "sparse radial sampling lbp for writer identification"
    - "deepwriterid an end to end online text independent writer identification system"
    - "rotation invariants of two dimensional curves based on iterated integrals"

Connected through hyperedge HE-3 (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"
    - "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"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep residual learning for image 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)