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: "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines"
  Abstract: Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

Connected through hyperedge HE-1 (induced by source paper "improving facial emotion recognition systems using gradient and laplacian images"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "facial expression recognition based on local region specific features and support vector machines"
    - "a deep learning perspective on the origin of facial expressions"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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"
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-3 (induced by source paper "advanced local motion patterns for macro and micro facial expression recognition"):
  with members:
    - "a deep learning perspective on the origin of facial expressions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
    - "reading hidden emotions spontaneous micro expression spotting and recognition"
    - "automatic facial expression recognition using features of salient facial patches"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-4 (induced by source paper "facial expression recognition using facial landmark detection and feature extraction via neural networks"):
  with members:
    - "facial expression recognition based on local region specific features and support vector machines"
    - "a deep learning perspective on the origin of facial expressions"
    - "automatic facial expression recognition using features of salient facial patches"

Connected through hyperedge HE-5 (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-6 (induced by source paper "sention a framework for sensing facial expressions"):
  with members:
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "dexpression deep convolutional neural network for expression recognition"
    - "automatic facial expression recognition using features of salient facial patches"
    - "a real time facial expression classification system using local binary patterns"

Connected through hyperedge HE-7 (induced by source paper "facial expression recognition in the wild using rich deep features"):
  with members:
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "face expression recognition and analysis the state of the art"

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)