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 "data selection for training semantic segmentation cnns with cross dataset weak supervision"):
  Abstract: Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data. We propose two methods for selecting the most relevant data with weak supervision. The first method is designed for finding visually similar images without the need of labels and is based on modeling image representations with a Gaussian Mixture Model (GMM). As a byproduct of GMM modeling, we present useful insights on characterizing the data generating distribution. The second method aims at finding images with high object diversity and requires only the bounding box labels. Both methods are developed in the context of automated driving and experimentation is conducted on Cityscapes and Open Images datasets. We demonstrate performance gains by reducing the amount of employed weakly labeled images up to 100 times for Open Images and up to 20 times for Cityscapes.

Includes members:
  Member "semantic redundancies in image classification datasets the 10 you don t need":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "subset sampling for progressive neural network learning")
  Member "on boosting semantic street scene segmentation with weak supervision":
    also connected through other hyperedges:
      (none)
  Member "are all training examples created equal an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "econas finding proxies for economical neural architecture search")
  Member "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep neural network perception models and robust autonomous driving systems")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "learning semantic segmentation with diverse supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comprehensive analysis of weakly supervised semantic segmentation in different image domains")
  Member "the open images dataset v4 unified image classification object detection and visual relationship detection at scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting the sibling head in object detector")
  Member "pixel level data augmentation for semantic image segmentation using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self ensembling with gan based data augmentation for domain adaptation in semantic 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)