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 equally valuable"
  Abstract: When learning a new concept, not all training examples may prove equally useful for training: some may have higher or lower training value than others. The goal of this paper is to bring to the attention of the vision community the following considerations: (1) some examples are better than others for training detectors or classifiers, and (2) in the presence of better examples, some examples may negatively impact performance and removing them may be beneficial. In this paper, we propose an approach for measuring the training value of an example, and use it for ranking and greedily sorting examples. We test our methods on different vision tasks, models, datasets and classifiers. Our experiments show that the performance of current state-of-the-art detectors and classifiers can be improved when training on a subset, rather than the whole training set.

Connected through hyperedge HE-1 (induced by source paper "dataset distillation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "few shot adversarial domain adaptation"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "data free knowledge distillation for deep neural networks"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "influence function based data poisoning attacks to top n recommender systems"):
  with members:
    - "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"
    - "byzantine resilient stochastic gradient descent for distributed learning a lipschitz inspired coordinate wise median approach"
    - "on the accuracy of influence functions for measuring group effects"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "attacking graph based classification via manipulating the graph structure"
    - "poisoning attacks to graph based recommender systems"
    - "data dropout optimizing training data for convolutional neural networks"
    - "byzantine robust distributed learning towards optimal statistical rates"

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"
    - "are all training examples created equal an empirical study"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "self paced deep learning for weakly supervised object detection"):
  with members:
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised object localization using size estimates"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "webly supervised learning of convolutional networks"
    - "contextual action recognition with r cnn"

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"
    - "are all training examples created equal an empirical study"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "empirical study of easy and hard examples in cnn training"):
  with members:
    - "an empirical study of example forgetting during deep neural network learning"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "using small proxy datasets to accelerate hyperparameter search"):
  with members:
    - "dataset distillation"
    - "not all samples are created equal deep learning with importance sampling"
    - "super convergence very fast training of residual networks using large learning rates"
    - "regularizing neural networks by penalizing confident output distributions"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "self paced learning with adaptive deep visual embeddings"):
  with members:
    - "deep metric learning with angular loss"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep metric learning via facility location"
    - "learning a metric for class conditional knn"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "online batch selection for faster training of neural networks"
    - "deep metric learning via lifted structured feature embedding"

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.CV(Computer Vision and Pattern Recognition)