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: "graph edge convolutional neural networks for skeleton based action recognition"
  Abstract: This paper investigates body bones from skeleton data for skeleton based action recognition. Body joints, as the direct result of mature pose estimation technologies, are always the key concerns of traditional action recognition methods. However, instead of joints, we humans naturally identify how the human body moves according to shapes, lengths and places of bones, which are more obvious and stable for observation. Hence given graphs generated from skeleton data, we propose to develop convolutions over graph edges that correspond to bones in human skeleton. We describe an edge by integrating its spatial neighboring edges to explore the cooperation between different bones, as well as its temporal neighboring edges to address the consistency of movements in an action. A graph edge convolutional neural network is then designed for skeleton based action recognition. Considering the complementarity between graph node convolution and graph edge convolution, we additionally construct two hybrid neural networks to combine graph node convolutional neural network and graph edge convolutional neural network using shared intermediate layers. Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our graph edge convolution is effective to capture characteristic of actions and our graph edge convolutional neural network significantly outperforms existing state-of-art skeleton based action recognition methods. Additionally, more performance improvements can be achieved by the hybrid networks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "robust deep graph based learning for binary classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "graph laplacian regularized graph convolutional networks for semi supervised learning"
    - "dimensionality driven learning with noisy labels"
    - "dynamic graph cnn for learning on point clouds"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "learning to reweight examples for robust deep learning"
    - "iterative learning with open set noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-3 (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"
    - "spatio temporal graph convolution 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-4 (induced by source paper "dwnet deep wide network for 3d action recognition"):
  with members:
    - "random vector functional link neural network based ensemble deep learning"
    - "graph neural networks a review of methods and applications"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "systems of natural language facilitated human robot cooperation a review"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

Connected through hyperedge HE-5 (induced by source paper "stacked spatio temporal graph convolutional networks for action segmentation"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "videos as space time region graphs"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "the description length of deep learning models"
    - "rgcnn regularized graph cnn for point cloud segmentation"
    - "situation recognition with graph neural 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)