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 using facial landmark detection and feature extraction via neural networks"):
  Abstract: The proposed framework in this paper has the primary objective of classifying the facial expression shown by a person. These classifiable expressions can be any one of the six universal emotions along with the neutral emotion. After the initial facial localization is performed, facial landmark detection and feature extraction are applied where in the landmarks are determined to be the fiducial features: the eyebrows, eyes, nose and lips. This is primarily done using the Sobel operator and the Hough transform followed by Shi Tomasi corner point detection. This leads to input feature vectors being formulated using Euclidean distances and trained into a Multi-Layer Perceptron (MLP) neural network in order to classify the expression being displayed. The results achieved have further dealt with higher uniformity in certain emotions and the inherently subjective nature of expression.

Includes members:
  Member "facial expression recognition based on local region specific features and support vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving facial emotion recognition systems using gradient and laplacian images")
  Member "a deep learning perspective on the origin of facial expressions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for human affect recognition insights and new developments")
  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 "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving facial emotion recognition systems using gradient and laplacian images")

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)