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: "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
  Abstract: Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition. For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show our method outperforms humans in the ME recognition task by a large margin, and achieves comparable performance to humans at the very challenging task of spotting and then recognizing spontaneous MEs.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "focal loss for dense object detection"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "temporal segment networks towards good practices for deep action recognition"
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "evaluation of the spatio temporal features and gan for micro expression recognition system"):
  with members:
    - "shallow triple stream three dimensional cnn ststnet for micro expression recognition"
    - "less is more micro expression recognition from video using apex frame"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "generative face completion"
    - "camera style adaptation for person re identification"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-3 (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"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "learning based video motion magnification"
    - "a review on facial micro expressions analysis datasets features and metrics"

Connected through hyperedge HE-4 (induced by source paper "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"):
  with members:
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "off apexnet on micro expression recognition system"
    - "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-5 (induced by source paper "shallow triple stream three dimensional cnn ststnet for micro expression recognition"):
  with members:
    - "off apexnet on micro expression recognition system"
    - "less is more micro expression recognition from video using apex frame"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "a boost in revealing subtle facial expressions a consolidated eulerian framework"):
  with members:
    - "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions"
    - "cross database micro expression recognition a benchmark"
    - "video acceleration magnification"
    - "micro expression spotting a benchmark"
    - "reading hidden emotions spontaneous micro expression spotting and recognition"

Connected through hyperedge HE-7 (induced by source paper "ads me anomaly detection system for micro expression spotting"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "micro expression detection in long videos using optical flow and recurrent neural networks"):
  with members:
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "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)