human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "evaluation of the spatio temporal features and gan for micro expression recognition system"):
  Abstract: Owing to the development and advancement of artificial intelligence, numerous works were established in the human facial expression recognition system. Meanwhile, the detection and classification of micro-expressions are attracting attentions from various research communities in the recent few years. In this paper, we first review the processes of a conventional optical-flow-based recognition system, which comprised of facial landmarks annotations, optical flow guided images computation, features extraction and emotion class categorization. Secondly, a few approaches have been proposed to improve the feature extraction part, such as exploiting GAN to generate more image samples. Particularly, several variations of optical flow are computed in order to generate optimal images to lead to high recognition accuracy. Next, GAN, a combination of Generator and Discriminator, is utilized to generate new "fake" images to increase the sample size. Thirdly, a modified state-of-the-art Convolutional neural networks is proposed. To verify the effectiveness of the the proposed method, the results are evaluated on spontaneous micro-expression databases, namely SMIC, CASME II and SAMM. Both the F1-score and accuracy performance metrics are reported in this paper.

Includes members:
  Member "shallow triple stream three dimensional cnn ststnet for micro expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extended local binary patterns for efficient and robust spontaneous facial micro expression recognition")
  Member "less is more micro expression recognition from video using apex frame":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "micro expression action unit detection withspatio temporal adaptive pooling")
  Member "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions")
  Member "liteflownet a lightweight convolutional neural network for optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "softmax splatting for video frame interpolation")
  Member "deep alignment network a convolutional neural network for robust face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition")
  Member "generative face completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "camera style adaptation for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "sparsity in dynamics of spontaneous subtle emotions analysis and application":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions")

Question: Please tell me which class the source paper 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)