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: "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
  Abstract: Inspired by the theory of Leitners learning box from the field of psychology, we propose DropSample, a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR). According to the principle of DropSample, each training sample is associated with a quota function that is dynamically adjusted on the basis of the classification confidence given by the DCNN softmax output. After a learning iteration, samples with low confidence will have a higher probability of being selected as training data in the next iteration; in contrast, well-trained and well-recognized samples with very high confidence will have a lower probability of being involved in the next training iteration and can be gradually eliminated. As a result, the learning process becomes more efficient as it progresses. Furthermore, we investigate the use of domain-specific knowledge to enhance the performance of DCNN by adding a domain knowledge layer before the traditional CNN. By adopting DropSample together with different types of domain-specific knowledge, the accuracy of HCCR can be improved efficiently. Experiments on the CASIA-OLHDWB 1.0, CASIA-OLHWDB 1.1, and ICDAR 2013 online HCCR competition datasets yield outstanding recognition rates of 97.33%, 97.06%, and 97.51% respectively, all of which are significantly better than the previous best results reported in the literature.

Connected through hyperedge HE-1 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "deep clustering for unsupervised learning of visual features"
    - "revisiting pre training an efficient training method for image classification"
    - "large scale visual active learning with deep probabilistic ensembles"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deep signature transforms"):
  with members:
    - "signatory differentiable computations of the signature and logsignature transforms on both cpu and gpu"
    - "the iisignature library efficient calculation of iterated integral signatures and log signatures"
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "generative moment matching networks"
    - "deepwriterid an end to end online text independent writer identification system"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "trajectory based radical analysis network for online handwritten chinese character recognition"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "modeling coverage for neural machine translation"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (induced by source paper "learning with rethinking recurrently improving convolutional neural networks through feedback"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "seven ways to improve example based single image super resolution"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "deeply recursive convolutional network for image super resolution"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "dropregion training of inception font network for high performance chinese font recognition"):
  with members:
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
    - "deepfont identify your font from an image"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (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-8 (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"
    - "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)