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: "emotional expression classification using time series kernels"
  Abstract: Estimation of facial expressions, as spatio-temporal processes, can take advantage of kernel methods if one considers facial landmark positions and their motion in 3D space. We applied support vector classification with kernels derived from dynamic time-warping similarity measures. We achieved over 99% accuracy - measured by area under ROC curve - using only the 'motion pattern' of the PCA compressed representation of the marker point vector, the so-called shape parameters. Beyond the classification of full motion patterns, several expressions were recognized with over 90% accuracy in as few as 5-6 frames from their onset, about 200 milliseconds.

Connected through hyperedge HE-1 (induced by source paper "continuous emotion recognition via deep convolutional autoencoder and support vector regressor"):
  with members:
    - "end to end multimodal emotion recognition using deep neural networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-2 (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "discriminatively trained latent ordinal model for video classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "expanded parts model for semantic description of humans in still images"
    - "unsupervised representation learning by sorting sequences"
    - "lomo latent ordinal model for facial analysis in videos"
    - "convolutional two stream network fusion for video action recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "videolstm convolves attends and flows for action recognition"
    - "self supervised video representation learning with odd one out networks"

Connected through hyperedge HE-4 (induced by source paper "ensemble of hankel matrices for face emotion recognition"):
  with members:
    - "using hankel matrices for dynamics based facial emotion recognition and pain detection"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "compressive acquisition of dynamic scenes"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-5 (induced by source paper "using hankel matrices for dynamics based facial emotion recognition and pain detection"):
  with members:
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "compressive acquisition of dynamic scenes"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-6 (induced by source paper "a generative restricted boltzmann machine based method for high dimensional motion data modeling"):
  with members:
    - "facial feature tracking under varying facial expressions and face poses based on restricted boltzmann machines"
    - "classification of sets using restricted boltzmann machines"

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)