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 "weakly supervised video moment retrieval from text queries"):
  Abstract: There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.

Includes members:
  Member "w talc weakly supervised temporal activity localization and classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "multilevel language and vision integration for text to clip retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "webly supervised joint embedding for cross modal image text retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "imram iterative matching with recurrent attention memory for cross modal image text retrieval")
  Member "rethinking the faster r cnn architecture for temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "learning two branch neural networks for image text matching tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "dual path convolutional image text embedding with instance loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "untrimmednets for weakly supervised action recognition and detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")

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)