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 "video re localization"):
  Abstract: Many methods have been developed to help people find the video contents they want efficiently. However, there are still some unsolved problems in this area. For example, given a query video and a reference video, how to accurately localize a segment in the reference video such that the segment semantically corresponds to the query video? We define a distinctively new task, namely \textbf{video re-localization}, to address this scenario. Video re-localization is an important emerging technology implicating many applications, such as fast seeking in videos, video copy detection, video surveillance, etc. Meanwhile, it is also a challenging research task because the visual appearance of a semantic concept in videos can have large variations. The first hurdle to clear for the video re-localization task is the lack of existing datasets. It is labor expensive to collect pairs of videos with semantic coherence or correspondence and label the corresponding segments. We first exploit and reorganize the videos in ActivityNet to form a new dataset for video re-localization research, which consists of about 10,000 videos of diverse visual appearances associated with localized boundary information. Subsequently, we propose an innovative cross gated bilinear matching model such that every time-step in the reference video is matched against the attentively weighted query video. Consequently, the prediction of the starting and ending time is formulated as a classification problem based on the matching results. Extensive experimental results show that the proposed method outperforms the competing methods. Our code is available at: this https URL.

Includes members:
  Member "bidirectional attentive fusion with context gating for dense video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "reconstruction network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "video based person re identification with accumulative motion context":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "global local temporal representations for video person re identification")
  Member "action tubelet detector for spatio temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grab fast and accurate sensor processing for cashier free shopping")
  Member "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "neural person search machines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "triplet online instance matching loss for person re identification")
  Member "the kinetics human action video dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "tall temporal activity localization via language query":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")

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)