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 "pointly supervised action localization"):
  Abstract: This paper strives for spatio-temporal localization of human actions in videos. In the literature, the consensus is to achieve localization by training on bounding box annotations provided for each frame of each training video. As annotating boxes in video is expensive, cumbersome and error-prone, we propose to bypass box-supervision. Instead, we introduce action localization based on point-supervision. We start from unsupervised spatio-temporal proposals, which provide a set of candidate regions in videos. While normally used exclusively for inference, we show spatio-temporal proposals can also be leveraged during training when guided by a sparse set of point annotations. We introduce an overlap measure between points and spatio-temporal proposals and incorporate them all into a new objective of a multiple instance learning optimization. During inference, we introduce pseudo-points, visual cues from videos, that automatically guide the selection of spatio-temporal proposals. We outline five spatial and one temporal pseudo-point, as well as a measure to best leverage pseudo-points at test time. Experimental evaluation on three action localization datasets shows our pointly-supervised approach (1) is as effective as traditional box-supervision at a fraction of the annotation cost, (2) is robust to sparse and noisy point annotations, (3) benefits from pseudo-points during inference, and (4) outperforms recent weakly-supervised alternatives. This leads us to conclude that points provide a viable alternative to boxes for action localization.

Includes members:
  Member "amtnet action micro tube regression by end to end trainable deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "why can t i dance in the mall learning to mitigate scene bias in action recognition")
  Member "extreme clicking for efficient object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "localizing actions from video labels and pseudo annotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tacnet transition aware context network for spatio temporal action detection")
  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 "spatio temporal action detection with cascade proposal and location anticipation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection")
  Member "weakly supervised object localization with multi fold multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "spatial aware object embeddings for zero shot localization and classification of actions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "pathtrack fast trajectory annotation with path supervision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mots multi object tracking and segmentation")

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)