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 "temporal action labeling using action sets"):
  Abstract: Action detection and temporal segmentation of actions in videos are topics of increasing interest. While fully supervised systems have gained much attention lately, full annotation of each action within the video is costly and impractical for large amounts of video data. Thus, weakly supervised action detection and temporal segmentation methods are of great importance. While most works in this area assume an ordered sequence of occurring actions to be given, our approach only uses a set of actions. Such action sets provide much less supervision since neither action ordering nor the number of action occurrences are known. In exchange, they can be easily obtained, for instance, from meta-tags, while ordered sequences still require human annotation. We introduce a system that automatically learns to temporally segment and label actions in a video, where the only supervision that is used are action sets. We evaluate our method on three datasets and show that it performs close to or on par with recent weakly supervised methods that require ordering constraints.

Includes members:
  Member "temporal action localization in untrimmed videos via multi stage cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "connectionist temporal modeling for weakly supervised action labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "spot on action localization from pointly supervised proposals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "recognizing fine grained and composite activities using hand centric features and script data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "convolutional two stream network fusion for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "end to end learning of action detection from frame glimpses in videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "video storytelling textual summaries for events")
  Member "an end to end generative framework for video segmentation and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation")
  Member "unsupervised learning from narrated instruction videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation")

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)