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: "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
  Abstract: Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps using a sliding window-based method, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MCFCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.10% and 97.15%, respectively, which are significantly better than the best result reported thus far in the literature.

Connected through hyperedge HE-1 (induced by source paper "aggregation cross entropy for sequence recognition"):
  with members:
    - "edit probability for scene text recognition"
    - "attention is all you need"
    - "scene text recognition with sliding convolutional character models"
    - "focusing attention towards accurate text recognition in natural images"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "hierarchical question image co attention for visual question answering"
    - "synthetic data for text localisation in natural images"

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 "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"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character 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 "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"
    - "sparse radial sampling lbp for writer identification"
    - "deepwriterid an end to end online text independent writer identification system"
    - "rotation invariants of two dimensional curves based on iterated integrals"

Connected through hyperedge HE-5 (induced by source paper "writer aware cnn for parsimonious hmm based offline handwritten chinese text recognition"):
  with members:
    - "aggregation cross entropy for sequence recognition"
    - "parsimonious hmms for offline handwritten chinese text recognition"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "channel pruning for accelerating very deep neural networks"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "accelerating very deep convolutional networks for classification and detection"

Connected through hyperedge HE-6 (induced by source paper "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "dynamic network surgery for efficient dnns"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "quantized convolutional neural networks for mobile devices"

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"

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)