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 "am i done predicting action progress in videos"):
  Abstract: In this paper we introduce the problem of predicting action progress in videos. We argue that this is an extremely important task because, on the one hand, it can be valuable for a wide range of applications and, on the other hand, it facilitates better action detection results. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make framewise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets. Additionally, we show that exploiting action progress it is also possible to improve spatio-temporal localization.

Includes members:
  Member "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "future state predicting lstm for early surgery type recognition")
  Member "action completion a temporal model for moment detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal cycle consistency learning")
  Member "guess where actor supervision for spatiotemporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refineloc iterative refinement for weakly supervised action localization")
  Member "temporal recurrent networks for online action detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "what actions are needed for understanding human actions in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evolving losses for unsupervised video representation learning")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  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)