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: "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
  Abstract: Currently, owing to the ubiquity of mobile devices, online handwritten Chinese character recognition (HCCR) has become one of the suitable choice for feeding input to cell phones and tablet devices. Over the past few years, larger and deeper convolutional neural networks (CNNs) have extensively been employed for improving character recognition performance. However, its substantial storage requirement is a significant obstacle in deploying such networks into portable electronic devices. To circumvent this problem, we propose a novel technique called DropWeight for pruning redundant connections in the CNN architecture. It is revealed that the proposed method not only treats streamlined architectures such as AlexNet and VGGNet well but also exhibits remarkable performance for deep residual network and inception network. We also demonstrate that global pooling is a better choice for building very compact online HCCR systems. Experiments were performed on the ICDAR-2013 online HCCR competition dataset using our proposed network, and it is found that the proposed approach requires only 0.57 MB for storage, whereas state-of-the-art CNN-based methods require up to 135 MB; meanwhile the performance is decreased only by 0.91%.

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"
    - "toward high performance online hccr"
    - "emnist an extension of mnist to handwritten letters"
    - "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 "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"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "a hybrid nn hmm modeling technique for online arabic handwriting 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)