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: "skepxels spatio temporal image representation of human skeleton joints for action recognition"
  Abstract: Human skeleton joints are popular for action analysis since they can be easily extracted from videos to discard background noises. However, current skeleton representations do not fully benefit from machine learning with CNNs. We propose "Skepxels" a spatio-temporal representation for skeleton sequences to fully exploit the "local" correlations between joints using the 2D convolution kernels of CNN. We transform skeleton videos into images of flexible dimensions using Skepxels and develop a CNN-based framework for effective human action recognition using the resulting images. Skepxels encode rich spatio-temporal information about the skeleton joints in the frames by maximizing a unique distance metric, defined collaboratively over the distinct joint arrangements used in the skeletal image. Moreover, they are flexible in encoding compound semantic notions such as location and speed of the joints. The proposed action recognition exploits the representation in a hierarchical manner by first capturing the micro-temporal relations between the skeleton joints with the Skepxels and then exploiting their macro-temporal relations by computing the Fourier Temporal Pyramids over the CNN features of the skeletal images. We extend the Inception-ResNet CNN architecture with the proposed method and improve the state-of-the-art accuracy by 4.4% on the large scale NTU human activity dataset. On the medium-sized N-UCLA and UTH-MHAD datasets, our method outperforms the existing results by 5.7% and 9.3% respectively.

Connected through hyperedge HE-1 (induced by source paper "finehand learning hand shapes for american sign language recognition"):
  with members:
    - "sign language recognition analysis using multimodal data"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "pose flow efficient online pose tracking"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "towards accurate multi person pose estimation in the wild"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "convolutional pose machines"

Connected through hyperedge HE-2 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics"):
  with members:
    - "action recognition for depth video using multi view dynamic images"
    - "compressed video action recognition"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "learning human pose models from synthesized data for robust rgb d action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-3 (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-4 (induced by source paper "being the center of attention a person context cnn framework for personality recognition"):
  with members:
    - "structural recurrent neural network srnn for group activity analysis"
    - "persemon a deep network for joint analysis of apparent personality emotion and their relationship"
    - "rgb d based human motion recognition with deep learning a survey"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "sign language recognition analysis using multimodal data"):
  with members:
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "a critical review of recurrent neural networks for sequence learning"
    - "differential recurrent neural networks for action recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "action attending graphic neural network"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "action recognition with visual attention on skeleton images"):
  with members:
    - "action attending graphic neural network"
    - "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"
    - "residual attention network for image classification"

Connected through hyperedge HE-8 (induced by source paper "ensemble one dimensional convolution neural networks for skeleton based action recognition"):
  with members:
    - "skeleton based action recognition using translation scale invariant image mapping and multi scale deep cnn"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "skeleton based action recognition using lstm and cnn"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

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)