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 "refineloc iterative refinement for weakly supervised action localization"):
  Abstract: Video action detectors are usually trained using video datasets with fully supervised temporal annotations. Building such video datasets is a heavily expensive task. To alleviate this problem, recent algorithms leverage weak labelling where videos are untrimmed and only a video-level label is available. In this paper, we propose RefineLoc, a new method for weakly-supervised temporal action localization. RefineLoc uses an iterative refinement approach by estimating and training on snippet-level pseudo ground truth at every iteration. We show the benefit of using such an iterative approach and present an extensive analysis of different pseudo ground truth generators. We show the effectiveness of our model on two standard action datasets, ActivityNet v1.2 and THUMOS14. RefineLoc equipped with a segment prediction-based pseudo ground truth generator improves the state-of-the-art in weakly-supervised temporal localization on the challenging and large-scale ActivityNet dataset by 4.2% and achieves comparable performance with state-of-the-art on THUMOS14.

Includes members:
  Member "3c net category count and center loss for weakly supervised action localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "slim densepose thrifty learning from sparse annotations and motion cues":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring dense pose to proximal animal classes")
  Member "unsupervised pre training of image features on non curated data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "ms tcn multi stage temporal convolutional network for action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "weakly supervised action localization with background modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "bmn boundary matching network for temporal action proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos")
  Member "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "derivative manipulation for general example weighting")

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)