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 "tvr a large scale dataset for video subtitle moment retrieval"):
  Abstract: We introduce a new multimodal retrieval task - TV show Retrieval (TVR), in which a short video moment has to be localized from a large video (with subtitle) corpus, given a natural language query. Different from previous moment retrieval tasks dealing with videos only, TVR requires the system to understand both the video and the associated subtitle text, making it a more realistic task. To support the study of this new task, we have collected a large-scale, high-quality dataset consisting of 108,965 queries on 21,793 videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal alignment. Strict qualification and post-annotation verification tests are applied to ensure the quality of the collected data. We present several baselines and a novel Cross-modal Moment Localization (XML) modular network for this new dataset and task. The proposed XML model surpasses all presented baselines by a large margin and with better efficiency, providing a strong starting point for future work. Extensive analysis experiments also show that incorporating both video and subtitle modules yields better performance than either alone. Lastly, we have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named the TV show Caption dataset (TVC). Here models need to jointly use the video and subtitle to generate a caption description. Both datasets are publicly available at this https URL

Includes members:
  Member "tvqa spatio temporal grounding for video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a multimodal target source classifier with attention branches to understand ambiguous instructions for fetching daily objects")
  Member "vatex a large scale high quality multilingual dataset for video and language research":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "normalized and geometry aware self attention network for image captioning")
  Member "temporal localization of moments in video collections with natural language":
    also connected through other hyperedges:
      (none)
  Member "roberta a robustly optimized bert pretraining approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "excl extractive clip localization using natural language descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "proposal free temporal moment localization of a natural language query in video using guided attention")
  Member "learning a text video embedding from incomplete and heterogeneous data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "man moment alignment network for natural language moment retrieval via iterative graph adjustment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "mattnet modular attention network for referring expression comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)