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: "optimized skeleton based action recognition via sparsified graph regression"
  Abstract: With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skeletons based on RNN or CNN, which has limited expressive power for irregular skeleton joints. While graph convolutional networks (GCN) have been proposed to address irregular graph-structured data, the fundamental graph construction remains challenging. In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the spatio-temporal variation in the data. As the graph representation is crucial to graph convolution, we first propose graph regression to statistically learn the underlying graph from multiple observations. In particular, we provide spatio-temporal modeling of skeletons and pose an optimization problem on the graph structure over consecutive frames, which enforces the sparsity of the underlying graph for efficient representation. The optimized graph not only connects each joint to its neighboring joints in the same frame strongly or weakly, but also links with relevant joints in the previous and subsequent frames. We then feed the optimized graph into the GCN along with the coordinates of the skeleton sequence for feature learning, where we deploy high-order and fast Chebyshev approximation of spectral graph convolution. Further, we provide analysis of the variation characterization by the Chebyshev approximation. Experimental results validate the effectiveness of the proposed graph regression and show that the proposed GR-GCN achieves the state-of-the-art performance on the widely used NTU RGB+D, UT-Kinect and SYSU 3D datasets.

Connected through hyperedge HE-1 (induced by source paper "semantics guided neural networks for efficient skeleton based human action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "eleatt rnn adding attentiveness to neurons in recurrent neural networks"
    - "deep high resolution representation learning for human pose estimation"
    - "learning deep bilinear transformation for fine grained image representation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "adversarial attack on skeleton based human action recognition"):
  with members:
    - "centrality graph convolutional networks for skeleton based action recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "multi label classification with label graph superimposing"):
  with members:
    - "centrality graph convolutional networks for skeleton based action recognition"
    - "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"
    - "a baseline for multi label image classification using an ensemble of deep convolutional neural networks"

Connected through hyperedge HE-4 (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"
    - "darts differentiable architecture search"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-5 (induced by source paper "making the invisible visible action recognition through walls and occlusions"):
  with members:
    - "centrality graph convolutional networks for skeleton based action recognition"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "deep covariance descriptors for facial expression recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "automatic analysis of facial expressions based on deep covariance trajectories"
    - "skeleton based human action recognition with global context aware attention lstm networks"

Connected through hyperedge HE-7 (induced by source paper "literature review of action recognition in the wild"):
  with members:
    - "human action recognition and prediction a survey"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

Connected through hyperedge HE-8 (induced by source paper "fatigue detection"):
  with members:
    - "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition"
    - "erel selection using morphological relation"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm 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)