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 "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.

Includes members:
  Member "online and offline handwritten chinese character recognition a comprehensive study and new benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint architecture and knowledge distillation in convolutional neural network for offline handwritten chinese text recognition")
  Member "drawing and recognizing chinese characters with recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep independently recurrent neural network indrnn")
  Member "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking deep active learning using unlabeled data at model training")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network")
  Member "deepwriterid an end to end online text independent writer identification system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "signatory differentiable computations of the signature and logsignature transforms on both cpu and gpu")
  Member "empirical evaluation of rectified activations in convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "soft root sign activation function")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)