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: "facial expression recognition method based on complexity perception classification algorithm"
  Abstract: The invention discloses a facial expression recognition method based on a complexity perception classification algorithm. Firstly, a deep convolution neural network based on improved residual blocks is designed to train pre-processed training data sets, and facial features of a person are extracted; according to the complexity perception classification algorithm, the complexity of the facial features of the person is evaluated, the training data sets are divided to an easy sample set and a difficult sample set, and the two classes of sub sample sets are trained respectively to obtain an easy sample classifier and a difficult sample classifier; in view of the two classes of sub sample sets, a binary-class sample complexity identification classifier is trained; and after the test data sets are subjected to preprocessing and facial feature extraction, the facial features of the person extracted by the test data sets are subjected to complexity identification through the sample complexityidentification classifier, the test data sets are inputted to the easy sample classifier and the difficult sample classifier to complete recognition on the class of a facial expression according to the complexity of the facial features of the person.

Connected through hyperedge HE-1 (induced by source paper "edgecnn convolutional neural network classification model with small inputs for edge computing"):
  with members:
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "on lightweight privacy preserving collaborative learning for internet of things objects"
    - "lep cnn a lightweight edge device assisted privacy preserving cnn inference solution for iot"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "visual saliency maps can apply to facial expression recognition"):
  with members:
    - "your eyes say you re lying an eye movement pattern analysis for face familiarity and deceptive cognition"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "facial expression recognition using visual saliency and deep learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"

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)