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: "a real time facial expression classification system using local binary patterns"
  Abstract: Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns(LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.

Connected through hyperedge HE-1 (induced by source paper "timeconvnets a deep time windowed convolution neural network design for real time video facial expression recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (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-3 (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"
    - "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 "automated alertness and emotion detection for empathic feedback during e learning"):
  with members:
    - "a vision based system for monitoring the loss of attention in automotive drivers"

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)