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 analysis under partial occlusion a survey"):
  Abstract: Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.

Includes members:
  Member "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions")
  Member "emotionet challenge recognition of facial expressions of emotion in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multitask emotion recognition with incomplete labels")
  Member "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "avec 2016 depression mood and emotion recognition workshop and challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for human affect recognition insights and new developments")
  Member "confidence weighted local expression predictions for occlusion handling in expression recognition and action unit detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "facial expressions analysis under occlusions based on specificities of facial motion propagation")
  Member "face expression recognition and analysis the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual attention mobdensenet damdnet for robust 3d face alignment")

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)