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 covariance descriptors for facial expression recognition"
  Abstract: In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial expression recognition. The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By performing the classification of the facial expressions using Gaussian kernel on SPD manifold, we show that the covariance descriptors computed on DCNN features are more efficient than the standard classification with fully connected layers and softmax. By implementing our approach using the VGG-face and ExpNet architectures with extensive experiments on the Oulu-CASIA and SFEW datasets, we show that the proposed approach achieves performance at the state of the art for facial expression recognition.

Connected through hyperedge HE-1 (induced by source paper "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "controllable image to video translation a case study on facial expression generation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-2 (induced by source paper "few shot learning with localization in realistic settings"):
  with members:
    - "covariance pooling for facial expression recognition"
    - "low shot learning from imaginary data"
    - "power normalizing second order similarity network for few shot learning"
    - "prototypical networks for few shot learning"
    - "extreme clicking for efficient object annotation"
    - "a simple neural attentive meta learner"
    - "residual attention network for image classification"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-3 (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"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "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-4 (induced by source paper "deep facial expression recognition a survey"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "geometry contrastive gan for facial expression transfer"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "covariance pooling for facial expression recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"
    - "relaxed spatio temporal deep feature aggregation for real fake expression prediction"

Connected through hyperedge HE-5 (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  with members:
    - "covariance pooling for facial expression recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "island loss for learning discriminative features in facial expression recognition"
    - "automatic recognition of facial displays of unfelt emotions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "a riemannian network for spd matrix learning"

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)