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 "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  Abstract: Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied because it can free us from costly annotating temporal boundaries of actions. One prevalent strategy is obtaining action score sequences over time and then truncating segments of scores higher than a fixed threshold at every kept snippet. However, the threshold is not modeled in the training process and manually setting the threshold introduces expert knowledge, which damages the coherence of systems and makes it unfair for comparisons. In this paper, we propose to adaptively set the threshold at each snippet to be its background score, which can be learned to predict (LPAT). In both training and testing time, the predicted threshold is leveraged to localize action segments and the scores of these segments are allocated for video classification. We also identify an important constraint to improve the confidence of generated proposals, and model it as a novel loss term, which facilitates the video classification loss to improve models' localization ability. As such, our LPAT model is able to generate accurate action proposals with only video-level supervision. Extensive experiments on two standard yet challenging datasets, i.e., THUMOS'14 and ActivityNet1.2, show significant improvement over state-of-the-art methods.

Includes members:
  Member "dmc net generating discriminative motion cues for fast compressed video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "listen to look action recognition by previewing audio")
  Member "segregated temporal assembly recurrent networks for weakly supervised multiple action detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "cascaded pyramid mining network for weakly supervised temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scale matters temporal scale aggregation network for precise action localization in untrimmed videos")
  Member "w talc weakly supervised temporal activity localization and classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "rethinking the faster r cnn architecture for temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "single shot temporal action detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sf net single frame supervision for temporal action localization")

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)