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: "centrality graph convolutional networks for skeleton based action recognition"
  Abstract: The topological structure of skeleton data plays a significant role in human action recognition. Combining the topological structure with graph convolutional networks has achieved remarkable performance. In existing methods, modeling the topological structure of skeleton data only considered the connections between the joints and bones, and directly use physical information. However, there exists an unknown problem to investigate the key joints, bones and body parts in every human action. In this paper, we propose the centrality graph convolutional networks to uncover the overlooked topological information, and best take advantage of the information to distinguish key joints, bones, and body parts. A novel centrality graph convolutional network firstly highlights the effects of the key joints and bones to bring a definite improvement. Besides, the topological information of the skeleton sequence is explored and combined to further enhance the performance in a four-channel framework. Moreover, the reconstructed graph is implemented by the adaptive methods on the training process, which further yields improvements. Our model is validated by two large-scale datasets, NTU-RGB+D and Kinetics, and outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "adversarial attack on skeleton based human action recognition"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "label universal targeted attack"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "graph edge convolutional neural networks for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "multi label classification with label graph superimposing"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a baseline for multi label image classification using an ensemble of deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "making the invisible visible action recognition through walls and occlusions"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "end to end learning of motion representation for video understanding"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "controllable image to video translation a case study on facial expression generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

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)