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 "r c3d region convolutional 3d network for temporal activity detection"):
  Abstract: We address the problem of activity detection in continuous, untrimmed video streams. This is a difficult task that requires extracting meaningful spatio-temporal features to capture activities, accurately localizing the start and end times of each activity. We introduce a new model, Region Convolutional 3D Network (R-C3D), which encodes the video streams using a three-dimensional fully convolutional network, then generates candidate temporal regions containing activities, and finally classifies selected regions into specific activities. Computation is saved due to the sharing of convolutional features between the proposal and the classification pipelines. The entire model is trained end-to-end with jointly optimized localization and classification losses. R-C3D is faster than existing methods (569 frames per second on a single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14. We further demonstrate that our model is a general activity detection framework that does not rely on assumptions about particular dataset properties by evaluating our approach on ActivityNet and Charades. Our code is available at this http URL.

Includes members:
  Member "untrimmednets for weakly supervised action recognition and detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  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 "temporal action localization in untrimmed videos via multi stage cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "hollywood in homes crowdsourcing data collection for activity understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  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 "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "actionness estimation using hybrid fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "actions as moving points")
  Member "untrimmed video classification for activity detection submission to activitynet challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "g tad sub graph localization for temporal action detection")

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)