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: "leveraging the path signature for skeleton based human action recognition"
  Abstract: Human action recognition in videos is one of the most challenging tasks in computer vision. One important issue is how to design discriminative features for representing spatial context and temporal dynamics. Here, we introduce a path signature feature to encode information from intra-frame and inter-frame contexts. A key step towards leveraging this feature is to construct the proper trajectories (paths) for the data steam. In each frame, the correlated constraints of human joints are treated as small paths, then the spatial path signature features are extracted from them. In video data, the evolution of these spatial features over time can also be regarded as paths from which the temporal path signature features are extracted. Eventually, all these features are concatenated to constitute the input vector of a fully connected neural network for action classification. Experimental results on four standard benchmark action datasets, J-HMDB, SBU Dataset, Berkeley MHAD, and NTURGB+D demonstrate that the proposed approach achieves state-of-the-art accuracy even in comparison with recent deep learning based models.

Connected through hyperedge HE-1 (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-2 (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"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text 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-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"
    - "sparse arrays of signatures for online character recognition"
    - "classification of curves in 2d and 3d via affine integral signatures"

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)