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 proposal based solution to spatio temporal action detection in untrimmed videos"):
  Abstract: Existing approaches for spatio-temporal action detection in videos are limited by the spatial extent and temporal duration of the actions. In this paper, we present a modular system for spatio-temporal action detection in untrimmed security videos. We propose a two stage approach. The first stage generates dense spatio-temporal proposals using hierarchical clustering and temporal jittering techniques on frame-wise object detections. The second stage is a Temporal Refinement I3D (TRI-3D) network that performs action classification and temporal refinement on the generated proposals. The object detection-based proposal generation step helps in detecting actions occurring in a small spatial region of a video frame, while temporal jittering and refinement helps in detecting actions of variable lengths. Experimental results on the spatio-temporal action detection dataset - DIVA - show the effectiveness of our system. For comparison, the performance of our system is also evaluated on the THUMOS14 temporal action detection dataset.

Includes members:
  Member "ctap complementary temporal action proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constraining temporal relationship for action localization")
  Member "rethinking the faster r cnn architecture for temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "bsn boundary sensitive network for temporal action proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "cascaded boundary regression for temporal action detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "grab fast and accurate sensor processing for cashier free shopping")

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)