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: "shallow triple stream three dimensional cnn ststnet for micro expression recognition"
  Abstract: In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.

Connected through hyperedge HE-1 (induced by source paper "evaluation of the spatio temporal features and gan for micro expression recognition system"):
  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"
    - "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-2 (induced by source paper "extended local binary patterns for efficient and robust spontaneous facial micro expression recognition"):
  with members:
    - "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"
    - "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"

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)