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: "enriched long term recurrent convolutional network for facial micro expression recognition"
  Abstract: Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN "sees" when predicting the micro-expression classes.

Connected through hyperedge HE-1 (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"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "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-2 (induced by source paper "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"):
  with members:
    - "off apexnet on micro expression recognition system"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "a review on facial micro expressions analysis datasets features and metrics"
    - "spatio temporal facial expression recognition using convolutional neural networks and conditional random fields"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "a survey of automatic facial micro expression analysis databases methods and challenges"):
  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"
    - "objective classes for micro facial expression recognition"
    - "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-4 (induced by source paper "micro attention for micro expression recognition"):
  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"
    - "objective classes for micro facial expression recognition"
    - "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"

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)