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: "spatio temporal covariance descriptors for action and gesture recognition"
  Abstract: We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions. We also show how the descriptors can be computed quickly through the use of integral video representations. Experiments on the UCF sport, CK+ facial expression and Cambridge hand gesture datasets indicate superior performance of the proposed method compared to several recent state-of-the-art techniques. The proposed method is robust and does not require additional processing of the videos, such as foreground detection, interest-point detection or tracking.

Connected through hyperedge HE-1 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition"):
  with members:
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "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"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-2 (induced by source paper "fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion"):
  with members:
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"
    - "real time and continuous hand gesture spotting an approach based on artificial neural networks"

Connected through hyperedge HE-3 (induced by source paper "lbvcnn local binary volume convolutional neural network for facial expression recognition from image sequences"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "lomo latent ordinal model for facial analysis in videos"
    - "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines"

Connected through hyperedge HE-4 (induced by source paper "microexpnet an extremely small and fast model for expression recognition from frontal face images"):
  with members:
    - "deep generative contrastive networks for facial expression recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "from facial expression recognition to interpersonal relation prediction"
    - "going deeper in facial expression recognition using deep neural networks"
    - "lomo latent ordinal model for facial analysis in videos"
    - "peak piloted deep network for facial expression recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "a novel space time representation on the positive semidefinite con for facial expression recognition"):
  with members:
    - "elastic functional coding of riemannian trajectories"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "survey on rgb 3d thermal and multimodal approaches for facial expression recognition history trends and affect related applications"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"

Connected through hyperedge HE-6 (induced by source paper "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"):
  with members:
    - "covariance of motion and appearance featuresfor spatio temporal recognition tasks"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"

Connected through hyperedge HE-7 (induced by source paper "locality preserving projection on spd matrix lie group algorithm and analysis"):
  with members:
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"

Connected through hyperedge HE-8 (induced by source paper "going deeper into action recognition a survey"):
  with members:
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "long term temporal convolutions for action recognition"
    - "towards good practices for very deep two stream convnets"
    - "training very deep networks"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"

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)