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 "budget aware activity detection with a recurrent policy network"):
  Abstract: In this paper, we address the challenging problem of effi- cient temporal activity detection in untrimmed long videos. While most recent work has focused and advanced the de- tection accuracy, the inference time can take seconds to minutes in processing one video, which is computationally prohibitive for many applications with tight runtime con- straints. This motivates our proposed budget-aware frame- work, which learns to perform activity detection by intel- ligently selecting a small subset of frames according to a specified time or computational budget. We formulate this problem as a Markov decision process, and adopt a recurrent network to model a policy for the frame selec- tion. To train the network, we employ a recurrent policy gradient approach to approximate the gradient of the non- decomposable and non-differentiable objective defined in our problem. In the extensive experiments on two bench- mark datasets, we achieve competitive detection accuracy, and more importantly, our approach is able to substantially reduce computational time and detect multiple activities in only 348ms for each untrimmed long video of THUMOS14 and ActivityNet.

Includes members:
  Member "action tubelet detector for spatio temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "r c3d region convolutional 3d network for temporal activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "temporal action localization in untrimmed videos via multi stage cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "temporal activity detection in untrimmed videos with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two stream region convolutional 3d network for temporal activity detection")
  Member "untrimmed video classification for activity detection submission to activitynet challenge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "g tad sub graph localization for temporal action detection")
  Member "end to end learning of action detection from frame glimpses in videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "video storytelling textual summaries for events")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)