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 "label and sample efficient training of vehicle object detector from sparsely labeled data"):
  Abstract: Self-driving vehicle vision systems must deal with an extremely broad and challenging set of scenes. They can potentially exploit an enormous amount of training data collected from vehicles in the field, but the volumes are too large to train offline naively. Not all training instances are equally valuable though, and importance sampling can be used to prioritize which training images to collect. This approach assumes that objects in images are labeled with high accuracy. To generate accurate labels in the field, we exploit the spatio-temporal coherence of vehicle video. We use a near-to-far labeling strategy by first labeling large, close objects in the video, and tracking them back in time to induce labels on small distant presentations of those objects. In this paper we demonstrate the feasibility of this approach in several steps. First, we note that an optimal subset (relative to all the objects encountered and labeled) of labeled objects in images can be obtained by importance sampling using gradients of the recognition network. Next we show that these gradients can be approximated with very low error using the loss function, which is already available when the CNN is running inference. Then, we generalize these results to objects in a larger scene using an object detection system. Finally, we describe a self-labeling scheme using object tracking. Objects are tracked back in time (near-to-far) and labels of near objects are used to check accuracy of those objects in the far field. We then evaluate the accuracy of models trained on importance sampled data vs models trained on complete data.

Includes members:
  Member "simple online and realtime tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi object tracking via end to end tracklet searching and ranking")
  Member "importance sampling for minibatches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning")
  Member "can we unify monocular detectors for autonomous driving by using the pixel wise semantic segmentation of cnns":
    also connected through other hyperedges:
      (none)
  Member "watch and learn semi supervised learning of object detectors from videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizable semi supervised learning method to estimate mass from sparsely annotated images")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "rich feature hierarchies for accurate object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)