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 "a novel apex time network for cross dataset micro expression recognition"):
  Abstract: The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. Whilst researchers working on such topics are more and more tending to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using apex frame is able to get rid of redundant information but the temporal evidence of micro-expression would be thereby left out. In this paper, we propose to do the recognition based on the spatial information from apex frame as well as on the temporal information from respective-adjacent frames. As such, a novel Apex-Time Network (ATNet) is proposed. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by adding the temporal information learned from adjacent frames around the apex frame. Specially, the model with such temporal information is more robust in cross-dataset validations.

Includes members:
  Member "less is more micro expression recognition from video using apex frame":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluation of the spatio temporal features and gan for micro expression recognition system")
  Member "micro attention for micro expression recognition":
    also connected through other hyperedges:
      (none)
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)