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: "advanced local motion patterns for macro and micro facial expression recognition"
  Abstract: In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative local motion patterns feature, with three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition. And, the third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variation and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-rank state-of-the-art methods for macro expression recognition.

Connected through hyperedge HE-1 (induced by source paper "what is the relationship between face alignment and facial expression recognition"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-2 (induced by source paper "optical flow techniques for facial expression analysis performance evaluation and improvements"):
  with members:
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "fast optical flow using dense inverse search"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-3 (induced by source paper "facial expressions analysis under occlusions based on specificities of facial motion propagation"):
  with members:
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "confidence weighted local expression predictions for occlusion handling in expression recognition and action unit detection"

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)