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: "deepwriterid an end to end online text independent writer identification system"
  Abstract: Owing to the rapid growth of touchscreen mobile terminals and pen-based interfaces, handwriting-based writer identification systems are attracting increasing attention for personal authentication, digital forensics, and other applications. However, most studies on writer identification have not been satisfying because of the insufficiency of data and difficulty of designing good features under various conditions of handwritings. Hence, we introduce an end-to-end system, namely DeepWriterID, employed a deep convolutional neural network (CNN) to address these problems. A key feature of DeepWriterID is a new method we are proposing, called DropSegment. It designs to achieve data augmentation and improve the generalized applicability of CNN. For sufficient feature representation, we further introduce path signature feature maps to improve performance. Experiments were conducted on the NLPR handwriting database. Even though we only use pen-position information in the pen-down state of the given handwriting samples, we achieved new state-of-the-art identification rates of 95.72% for Chinese text and 98.51% for English text.

Connected through hyperedge HE-1 (induced by source paper "signatory differentiable computations of the signature and logsignature transforms on both cpu and gpu"):
  with members:
    - "deep signature transforms"
    - "learning stochastic differential equations using rnn with log signature features"
    - "the iisignature library efficient calculation of iterated integral signatures and log signatures"

Connected through hyperedge HE-2 (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"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "generative moment matching networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "toward high performance online hccr"
    - "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"
    - "rotation invariants of two dimensional curves based on iterated integrals"

Connected through hyperedge HE-4 (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"
    - "toward high performance online hccr"
    - "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"

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 "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"
    - "empirical evaluation of rectified activations in convolutional network"
    - "going deeper with convolutions"
    - "fractional max pooling"

Connected through hyperedge HE-7 (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"
    - "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"
    - "sparse arrays of signatures for online character recognition"

Connected through hyperedge HE-8 (induced by source paper "drawing and recognizing chinese characters with recurrent neural network"):
  with members:
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "character level chinese writer identification using path signature feature dropstroke and deep cnn"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "describing multimedia content using attention based encoder decoder networks"

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)