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: "objective classes for micro facial expression recognition"
  Abstract: Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.

Connected through hyperedge HE-1 (induced by source paper "enriched long term recurrent convolutional network for facial micro expression recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "less is more micro expression recognition from video using apex frame"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a survey of automatic facial micro expression analysis databases methods and challenges"):
  with members:
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "less is more micro expression recognition from video using apex frame"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "learning a target sample re generator for cross database micro expression recognition"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "objective micro facial movement detection using facs based regions and baseline evaluation"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "micro attention for micro expression recognition"):
  with members:
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "less is more micro expression recognition from video using apex frame"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "residual attention network for image classification"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a review on facial micro expressions analysis datasets features and metrics"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "learning a target sample re generator for cross database micro expression recognition"
    - "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
    - "going deeper with convolutions"
    - "learning spatiotemporal features with 3d convolutional networks"

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)