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 "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"):
  Abstract: Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion of a person, as it is a spontaneous reaction expressed through human face. Despite of a few attempts made for recognizing micro-expressions, still the problem is far from being a solved problem, which is depicted by the poor rate of accuracy shown by the state-of-the-art methods. A few CNN based approaches are found in the literature to recognize micro-facial expressions from still images. Whereas, a spontaneous micro-expression video contains multiple frames that have to be processed together to encode both spatial and temporal information. This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework. The MicroExpSTCNN considers the full spatial information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion of the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and SMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms the state-of-the-art methods.

Includes members:
  Member "enriched long term recurrent convolutional network for facial 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 "off apexnet on micro expression recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shallow triple stream three dimensional cnn ststnet for micro expression recognition")
  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 "a performance evaluation of convolutional neural networks for face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep spatial gradient and temporal depth learning for face anti spoofing")
  Member "a review on facial micro expressions analysis datasets features and metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extended local binary patterns for efficient and robust spontaneous facial micro expression recognition")
  Member "spatio temporal facial expression recognition using convolutional neural networks and conditional random fields":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deeper look at facial expression dataset bias")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring 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)