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 "rethinking the faster r cnn architecture for temporal action localization"):
  Abstract: We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations; (2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields; and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS'14 detection benchmark and competitive performance on ActivityNet challenge.

Includes members:
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "single shot temporal action detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "untrimmednets for weakly supervised action recognition and detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "cascaded boundary regression for temporal action detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "temporal context network for activity localization in videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "action tubelet detector for spatio temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grab fast and accurate sensor processing for cashier free shopping")
  Member "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised temporal action localization using deep metric learning")

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)