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 "less is more an exploration of data redundancy with active dataset subsampling"):
  Abstract: Deep Neural Networks (DNNs) often rely on very large datasets for training. Given the large size of such datasets, it is conceivable that they contain certain samples that either do not contribute or negatively impact the DNN's performance. If there is a large number of such samples, subsampling the training dataset in a way that removes them could provide an effective solution to both improve performance and reduce training time. In this paper, we propose an approach called Active Dataset Subsampling (ADS), to identify favorable subsets within a dataset for training using ensemble based uncertainty estimation. When applied to three image classification benchmarks (CIFAR-10, CIFAR-100 and ImageNet) we find that there are low uncertainty subsets, which can be as large as 50% of the full dataset, that negatively impact performance. These subsets are identified and removed with ADS. We demonstrate that datasets obtained using ADS with a lightweight ResNet-18 ensemble remain effective when used to train deeper models like ResNet-101. Our results provide strong empirical evidence that using all the available data for training can hurt performance on large scale vision tasks.

Includes members:
  Member "billion scale semi supervised learning for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "semantic redundancies in image classification datasets the 10 you don t need":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "subset sampling for progressive neural network learning")
  Member "are all training examples created equal an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "econas finding proxies for economical neural architecture search")
  Member "practical obstacles to deploying active learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mots multi object tracking and segmentation")
  Member "adaptive semantic segmentation with a strategic curriculum of proxy labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation")
  Member "an empirical study of example forgetting during deep neural network learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation")
  Member "large scale visual active learning with deep probabilistic ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "viewal active learning with viewpoint entropy for semantic segmentation")
  Member "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "training data distribution search with ensemble active learning")

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.LG(Machine Learning)