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 "selection via proxy efficient data selection for deep learning"):
  Abstract: Data selection methods such as active learning and core-set selection are useful tools for machine learning on large datasets, but they can be prohibitively expensive to apply in deep learning. Unlike in other areas of machine learning, the feature representations that these techniques depend on are learned in deep learning rather than given, which takes a substantial amount of training time. In this work, we show that we can significantly improve the computational efficiency of data selection in deep learning by using a much smaller proxy model to perform data selection for tasks that will eventually require a large target model (e.g., selecting data points to label for active learning). In deep learning, we can scale down models by removing hidden layers or reducing their dimension to create proxies that are an order of magnitude faster. Although these small proxy models have significantly higher error, we find that they empirically provide useful rankings for data selection that have a high correlation with those of larger models. We evaluate this "selection via proxy" (SVP) approach on several data selection tasks. For active learning, applying SVP to Sener and Savarese [2018]'s recent method for active learning in deep learning gives a 4x improvement in execution time while yielding the same model accuracy. For core-set selection, we show that a proxy model that trains 10x faster than a target ResNet164 model on CIFAR10 can be used to remove 50% of the training data without compromising the accuracy of the target model, making end-to-end training time improvements via core-set selection possible.

Includes members:
  Member "batchbald efficient and diverse batch acquisition for deep bayesian active learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "task aware variational adversarial active learning")
  Member "weight agnostic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "how transferable are the datasets collected by active learners":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mots multi object tracking and segmentation")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "on the relationship between data efficiency and error for uncertainty sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive region based active learning")
  Member "the lottery ticket hypothesis finding sparse trainable neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "active learning for interactive neural machine translation of data streams":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self regulated interactive sequence to sequence learning")
  Member "an empirical study of example forgetting during deep neural network learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance 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.LG(Machine Learning)