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 "wslln weakly supervised natural language localization networks"):
  Abstract: We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries. To learn the correspondence between visual segments and texts, most previous methods require temporal coordinates (start and end times) of events for training, which leads to high costs of annotation. WSLLN relieves the annotation burden by training with only video-sentence pairs without accessing to temporal locations of events. With a simple end-to-end structure, WSLLN measures segment-text consistency and conducts segment selection (conditioned on the text) simultaneously. Results from both are merged and optimized as a video-sentence matching problem. Experiments on ActivityNet Captions and DiDeMo demonstrate that WSLLN achieves state-of-the-art performance.

Includes members:
  Member "startnet online detection of action start in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised traffic accident detection in first person videos")
  Member "man moment alignment network for natural language moment retrieval via iterative graph adjustment":
    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 "weakly supervised dense event captioning in videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal dense video captioning")
  Member "temporal recurrent networks for online action detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "to find where you talk temporal sentence localization in video with attention based location regression":
    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 "rethinking the faster r cnn architecture for temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "c wsl count guided weakly supervised localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object instance mining for weakly supervised object detection")

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)