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 "yotube searching action proposal via recurrent and static regression networks"):
  Abstract: In this paper, we propose  YoTube —a novel deep learning framework for generating action proposals in untrimmed videos, where each action proposal corresponds to a spatial-temporal tube that potentially locates one human action. Most of the existing works generate proposals by clustering low-level features or linking image proposals, which ignore the interplay between long-term temporal context and short-term cues. Different from these works, our method considers the interplay by designing a new recurrent  YoTube  detector and static  YoTube  detector. The recurrent  YoTube  detector sequentially regresses candidate bounding boxes using Recurrent Neural Network learned long-term temporal contexts. The static  YoTube  detector produces bounding boxes using rich appearance cues in every single frame. To fully exploit the complementary appearance, motion, and temporal context, we train the recurrent and static detector using RGB (Color) and flow information. Moreover, we fuse the corresponding outputs of the detectors to produce accurate and robust proposal boxes and obtain the final action proposals by linking the proposal boxes using dynamic programming with a novel path trimming method. Benefiting from the pipeline of our method, the untrimmed video could be effectively and efficiently handled. Extensive experiments on the challenging UCF-101, UCF-Sports, and JHMDB datasets show superior performance of the proposed method compared with the state of the arts.

Includes members:
  Member "rgbd salient object detection via deep fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection")
  Member "r c3d region convolutional 3d network for temporal activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "deep learning for video classification and captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  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 "searching action proposals via spatial actionness estimation and temporal path inference and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "video summarization via actionness ranking")
  Member "object detection from video tubelets with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking temporal object detection from robotic perspectives")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deep learning for detecting multiple space time action tubes in videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "actions as moving points")

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)