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: "deep learning on lie groups for skeleton based action recognition"
  Abstract: In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature dimensionality, the architecture is equipped with rotation pooling layers for the elements on the Lie group. Furthermore, we propose a logarithm mapping layer to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification. Evaluations of the proposed network for standard 3D human action recognition datasets clearly demonstrate its superiority over existing shallow Lie group feature learning methods as well as most conventional deep learning methods.

Connected through hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "spatio temporal graph convolution for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "skeleton based online action prediction using scale selection network"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "skeleton boxes solving skeleton based action detection with a single deep convolutional neural network"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"

Connected through hyperedge HE-5 (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-6 (induced by source paper "deep learning for 2d and 3d rotatable data an overview of methods"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "learning local rgb to cad correspondences for object pose estimation"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "3d human pose estimation with siamese equivariant embedding"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-7 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction"):
  with members:
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deep representation learning for human motion prediction and classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "on human motion prediction using recurrent neural networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-8 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "a deep learning perspective on the origin of facial expressions"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spontaneous subtle expression detection and recognition based on facial strain"

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)