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 "detecting the starting frame of actions in video"):
  Abstract: To understand causal relationships between events in the world, it is useful to pinpoint when actions occur in videos and to examine the state of the world at and around that time point. For example, one must accurately detect the start of an audience response -- laughter in a movie, cheering at a sporting event -- to understand the cause of the reaction. In this work, we focus on the problem of accurately detecting action starts rather than isolated events or action ends. We introduce a novel structured loss function based on matching predictions to true action starts that is tailored to this problem; it more heavily penalizes extra and missed action start detections over small misalignments. Recurrent neural networks are used to minimize a differentiable approximation of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was labeled by experts for the purpose of neuroscience research on causally relating neural activity to behavior. On this dataset, we demonstrate that the structured loss leads to significantly higher accuracy than a baseline of mean-squared error loss.

Includes members:
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "r c3d region convolutional 3d network for temporal activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "turn tap temporal unit regression network for temporal action proposals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constraining temporal relationship for action localization")
  Member "the thumos challenge on action recognition for videos in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "temporal segment networks towards good practices for deep action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "end to end people detection in crowded scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "set structured latent representations")

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)