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: "action attending graphic neural network"
  Abstract: The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision. In this paper, we propose a fully end-to-end action-attending graphic neural network (A2GNN) for skeleton-based action recognition, in which each irregular skeleton is structured as an undirected attribute graph. To extract high-level semantic representation from skeletons, we perform the local spectral graph filtering on the constructed attribute graphs like the standard image convolution operation. Considering not all joints are informative for action analysis, we design an action-attending layer to detect those salient action units by adaptively weighting skeletal joints. Herein, the filtering responses are parameterized into a weighting function irrelevant to the order of input nodes. To further encode continuous motion variations, the deep features learnt from skeletal graphs are gathered along consecutive temporal slices and then fed into a recurrent gated network. Finally, the spectral graph filtering, action-attending, and recurrent temporal encoding are integrated together to jointly train for the sake of robust action recognition as well as the intelligibility of human actions. To evaluate our A2GNN, we conduct extensive experiments on four benchmark skeleton-based action datasets, including the large-scale challenging NTU RGB+D dataset. The experimental results demonstrate that our network achieves the state-of-the-art performances.

Connected through hyperedge HE-1 (induced by source paper "human action performance using deep neuro fuzzy recurrent attention model"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "human action recognition and prediction a survey"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "action recognition with spatio temporal visual attention on skeleton image sequences"):
  with members:
    - "action recognition with visual attention on skeleton images"
    - "densepose dense human pose estimation in the wild"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "the kinetics human action video dataset"
    - "a new representation of skeleton sequences for 3d action recognition"

Connected through hyperedge HE-3 (induced by source paper "context dependent diffusion network for visual relationship detection"):
  with members:
    - "tensor graph convolutional neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-4 (induced by source paper "action recognition with visual attention on skeleton images"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "residual attention network for image classification"

Connected through hyperedge HE-5 (induced by source paper "gaussian induced convolution for graphs"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "tensor graph convolutional neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "shift aggregate extract networks"
    - "spectral filter tracking"
    - "learning graph representations with embedding propagation"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal graph convolution for skeleton based action recognition"):
  with members:
    - "deep manifold to manifold transforming network for action recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "gated graph sequence neural networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "learning convolutional neural networks for graphs"
    - "a riemannian network for spd matrix learning"
    - "structural rnn deep learning on spatio temporal graphs"

Connected through hyperedge HE-7 (induced by source paper "tensor graph convolutional neural network"):
  with members:
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "deep learning on lie groups for skeleton based action recognition"
    - "semi supervised classification with graph convolutional networks"
    - "matrix completion on graphs"
    - "discrete signal processing on graphs"
    - "spectral networks and locally connected networks on graphs"

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)