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 "look closer to ground better weakly supervised temporal grounding of sentence in video"):
  Abstract: In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video. Specifically, given an untrimmed video and a query sentence, our goal is to localize a temporal segment in the video that semantically corresponds to the query sentence, with no reliance on any temporal annotation during training. We propose a two-stage model to tackle this problem in a coarse-to-fine manner. In the coarse stage, we first generate a set of fixed-length temporal proposals using multi-scale sliding windows, and match their visual features against the sentence features to identify the best-matched proposal as a coarse grounding result. In the fine stage, we perform a fine-grained matching between the visual features of the frames in the best-matched proposal and the sentence features to locate the precise frame boundary of the fine grounding result. Comprehensive experiments on the ActivityNet Captions dataset and the Charades-STA dataset demonstrate that our two-stage model achieves compelling performance.

Includes members:
  Member "weakly supervised video moment retrieval from text queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  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 "mattnet modular attention network for referring expression comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "multilevel language and vision integration for text to clip retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "weakly supervised video object grounding from text by loss weighting and object interaction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos")

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)