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: "sparse arrays of signatures for online character recognition"
  Abstract: In mathematics the signature of a path is a collection of iterated integrals, commonly used for solving dierential equations. We show that the path signature, used as a set of features for consumption by a convolutional neural network (CNN), improves the accuracy of online character recognition|that is the task of reading characters represented as a collection of paths. Using datasets of letters, numbers, Assamese and Chinese characters, we show that the rst, second, and even the third iterated integrals contain useful information for consumption by a CNN. On the CASIA-OLHWDB1.1 3755 Chinese character dataset, our approach gave a test error of 3.58%, compared with 5.61%[4] for a traditional CNN. A CNN trained on the CASIA-OLHWDB1.0-1.2 datasets won the ICDAR2013 Online Isolated Chinese Character recognition competition. Computationally, we have developed a sparse CNN implementation that make it practical to train CNNs with many layers of maxpooling. Extending the MNIST dataset by translations, our sparse CNN gets a test error of 0.31%.

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"
    - "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-2 (induced by source paper "learning stochastic differential equations using rnn with log signature features"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton based gesture recognition using several fully connected layers with path signature features and temporal transformer module"
    - "the iisignature library efficient calculation of iterated integral signatures and log signatures"
    - "neural ordinary differential equations"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-3 (induced by source paper "invariants of multidimensional time series based on their iterated integral signature"):
  with members:
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "leveraging the path signature for skeleton based human action recognition"
    - "classification of curves in 2d and 3d via affine integral signatures"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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"

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"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "deepwriterid an end to end online text independent writer identification system"
    - "empirical evaluation of rectified activations in convolutional network"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "online signature verification using recurrent neural network and length normalized path signature"):
  with members:
    - "draw a recurrent neural network for image generation"
    - "deepwriterid an end to end online text independent writer identification system"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "deep metric learning using triplet network"
    - "rotation invariants of two dimensional curves based on iterated integrals"

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)