human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  Abstract: In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Convolutional Neural Network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By conducting the classification of static facial expressions using Support Vector Machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that both the proposed static and dynamic approaches achieve state-of-the-art performance for facial expression recognition outperforming many recent approaches.

Includes members:
  Member "deep covariance descriptors for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets")
  Member "covariance pooling for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "a novel geometric framework on gram matrix trajectories for human behavior understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "human action recognition with multi laplacian graph convolutional networks")
  Member "island loss for learning discriminative features in facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")
  Member "automatic recognition of facial displays of unfelt emotions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chalearn looking at people isogd and congd large scale rgb d gesture recognition")
  Member "facenet2expnet regularizing a deep face recognition net for expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "going deeper in facial expression recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "peak piloted deep network for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint deep learning of facial expression synthesis and recognition")

Question: Please tell me which class the source paper 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)