human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "are all training examples created equal an empirical study"
  Abstract: Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for training. In this paper, we propose a gradient-based importance measure that we use to empirically analyze relative importance of training images in four datasets of varying complexity. We find that in some cases, a small subsample is indeed sufficient for training. For other datasets, however, the relative differences in importance are negligible. These results have important implications for active learning on deep networks. Additionally, our analysis method can be used as a general tool to better understand diversity of training examples in datasets.

Connected through hyperedge HE-1 (induced by source paper "econas finding proxies for economical neural architecture search"):
  with members:
    - "detnas backbone search for object detection"
    - "searching for mobilenetv3"
    - "nas fcos fast neural architecture search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "subset sampling for progressive neural network learning"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "data driven neural architecture learning for financial time series forecasting"
    - "progressive operational perceptron with memory"
    - "progressive learning for systematic design of large neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "automl a survey of the state of the art"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-4 (induced by source paper "adapting convolutional neural networks for geographical domain shift"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep visual domain adaptation a survey"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "cycada cycle consistent adversarial domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-5 (induced by source paper "semantic redundancies in image classification datasets the 10 you don t need"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "tensor2tensor for neural machine translation"
    - "not all samples are created equal deep learning with importance sampling"
    - "learning from less data diversified subset selection and active learning in image classification tasks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-6 (induced by source paper "sampling bias in deep active classification an empirical study"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "practical obstacles to deploying active learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "an empirical study of example forgetting during deep neural network learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "when will gradient methods converge to max margin classifier under relu models"

Connected through hyperedge HE-7 (induced by source paper "less is more an exploration of data redundancy with active dataset subsampling"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "practical obstacles to deploying active learning"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-8 (induced by source paper "data selection for training semantic segmentation cnns with cross dataset weak supervision"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "on boosting semantic street scene segmentation with weak supervision"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "learning semantic segmentation with diverse supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "pixel level data augmentation for semantic image segmentation using generative adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Question: Please tell me which class the center node 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)