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 "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.

Includes members:
  Member "going deeper in facial expression recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "automatic facial expression recognition using features of salient facial patches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "deep learning using linear support vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed precision deep learning based on computational memory")

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)