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 "what is the relationship between face alignment and facial expression recognition"):
  Abstract: Face expression recognition is still a complex task, particularly due to the presence of head pose variations. Although face alignment approaches are becoming increasingly accurate for characterizing facial regions, it is important to consider the impact of these approaches when they are used for other related tasks such as head pose registration or facial expression recognition. In this paper, we compare the performance of recent face alignment approaches to highlight the most appropriate techniques for preserving facial geometry when correcting the head pose variation. Also, we highlight the most suitable techniques that locate facial landmarks in the presence of head pose variations and facial expressions.

Includes members:
  Member "face alignment in full pose range a 3d total solution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "advanced local motion patterns for macro and micro facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optical flow techniques for facial expression analysis performance evaluation and improvements")
  Member "towards highly accurate and stable face alignment for high resolution videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "anti confusing region aware network for human pose estimation")
  Member "red net a recurrent encoder decoder network for video based face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "laplace landmark localization")
  Member "a comprehensive performance evaluation of deformable face tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "facial landmark detection a literature survey")
  Member "style aggregated network for facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kpnet towards minimal face detector")
  Member "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kpnet towards minimal face detector")
  Member "deep alignment network a convolutional neural network for robust face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition")

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)