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: "spatio temporal graph convolution for skeleton based action recognition"
  Abstract: Variations of human body skeletons may be considered as dynamic graphs, which are generic data representation for numerous real-world applications. In this paper, we propose a spatio-temporal graph convolution (STGC) approach for assembling the successes of local convolutional filtering and sequence learning ability of autoregressive moving average. To encode dynamic graphs, the constructed multi-scale local graph convolution filters, consisting of matrices of local receptive fields and signal mappings, are recursively performed on structured graph data of temporal and spatial domain. The proposed model is generic and principled as it can be generalized into other dynamic models. We theoretically prove the stability of STGC and provide an upper-bound of the signal transformation to be learnt. Further, the proposed recursive model can be stacked into a multi-layer architecture. To evaluate our model, we conduct extensive experiments on four benchmark skeleton-based action datasets, including the large-scale challenging NTU RGB+D. The experimental results demonstrate the effectiveness of our proposed model and the improvement over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "driver drowsiness detection using condition adaptive representation learning framework"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "learning graph convolutional network for skeleton based human action recognition by neural searching"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton based action recognition with multi stream adaptive graph convolutional networks"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "densefusion 6d object pose estimation by iterative dense fusion"

Connected through hyperedge HE-2 (induced by source paper "a comparative review of recent kinect based action recognition algorithms"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "higher order pooling of cnn features via kernel linearization for action recognition"

Connected through hyperedge HE-3 (induced by source paper "learning graph convolutional network for skeleton based human action recognition by neural searching"):
  with members:
    - "video action recognition via neural architecture searching"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition"):
  with members:
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"

Connected through hyperedge HE-5 (induced by source paper "classifying signals on irregular domains via convolutional cluster pooling"):
  with members:
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "st unet a spatio temporal u network for graph structured time series modeling"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "large scale learnable graph convolutional networks"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "dilated recurrent neural networks"
    - "predicting citywide crowd flows using deep spatio temporal residual networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-7 (induced by source paper "skeleton based hand gesture recognition by learning spd matrices with neural networks"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-8 (induced by source paper "optimized skeleton based action recognition via sparsified graph regression"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "dynamic graph cnn for learning on point clouds"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "sentence state lstm for text representation"
    - "cross sentence n ary relation extraction with graph lstms"
    - "conversation modeling on reddit using a graph structured lstm"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional 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)