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: "developing the path signature methodology and its application to landmark based human action recognition"
  Abstract: Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for the spatial structure and temporal dynamics. To this end, we regard the evolving landmark data as a high-dimensional path and apply non-linear path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events. We do not extract signature features from the raw path, rather we propose path disintegrations and path transformations as preprocessing steps. Path disintegrations turn a high-dimensional path linearly into a collection of lower-dimensional paths; some of these paths are in pose space while others are defined over a multiscale collection of temporal intervals. Path transformations decorate the paths with additional coordinates in standard ways to allow the truncated signatures of transformed paths to expose additional features. For spatial representation, we apply the signature transform to vectorize the paths that arise out of pose disintegration, and for temporal representation, we apply it again to describe this evolving vectorization. Finally, all the features are collected together to constitute the input vector of a linear single-hidden-layer fully-connected network for classification. Experimental results on four datasets demonstrated that the proposed feature set with only a linear shallow network and Dropconnect is effective and achieves comparable state-of-the-art results to the advanced deep networks, and meanwhile, is capable of interpretation.

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 "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"
    - "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"
    - "deepwriterid an end to end online text independent writer identification system"
    - "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"
    - "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"
    - "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-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"
    - "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"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"

Connected through hyperedge HE-5 (induced by source paper "invariants of multidimensional time series based on their iterated integral signature"):
  with members:
    - "leveraging the path signature for skeleton 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)