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: "a survey of automatic facial micro expression analysis databases methods and challenges"
  Abstract: Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today. Although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.

Connected through hyperedge HE-1 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "a deep learning perspective on the origin of facial expressions"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-2 (induced by source paper "extended local binary patterns for efficient and robust spontaneous facial micro expression recognition"):
  with members:
    - "shallow triple stream three dimensional cnn ststnet for micro expression recognition"
    - "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"
    - "a novel apex time network for cross dataset micro expression recognition"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "learning based video motion magnification"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "a review on facial micro expressions analysis datasets features and metrics"

Connected through hyperedge HE-3 (induced by source paper "micro expression detection in long videos using optical flow and recurrent neural networks"):
  with members:
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "objective micro facial movement detection using facs based regions and baseline evaluation"

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)