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: "biased importance sampling for deep neural network training"
  Abstract: Importance sampling has been successfully used to accelerate stochastic optimization in many convex problems. However, the lack of an efficient way to calculate the importance still hinders its application to Deep Learning. #R##N#In this paper, we show that the loss value can be used as an alternative importance metric, and propose a way to efficiently approximate it for a deep model, using a small model trained for that purpose in parallel. #R##N#This method allows in particular to utilize a biased gradient estimate that implicitly optimizes a soft max-loss, and leads to better generalization performance. While such method suffers from a prohibitively high variance of the gradient estimate when using a standard stochastic optimizer, we show that when it is combined with our sampling mechanism, it results in a reliable procedure. #R##N#We showcase the generality of our method by testing it on both image classification and language modeling tasks using deep convolutional and recurrent neural networks. In particular, our method results in 30% faster training of a CNN for CIFAR10 than when using uniform sampling.

Connected through hyperedge HE-1 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "safe adaptive importance sampling"
    - "federated optimization distributed machine learning for on device intelligence"
    - "online learning to sample"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-2 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "deep clustering for unsupervised learning of visual features"
    - "revisiting pre training an efficient training method for image classification"
    - "large scale visual active learning with deep probabilistic ensembles"

Connected through hyperedge HE-3 (induced by source paper "accelerating deep learning by focusing on the biggest losers"):
  with members:
    - "autoassist a framework to accelerate training of deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "not all samples are created equal deep learning with importance sampling"
    - "learning to reweight examples for robust deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "distribution density tails and outliers in machine learning metrics and applications"):
  with members:
    - "less is more culling the training set to improve robustness of deep neural networks"
    - "data dropout optimizing training data for convolutional neural networks"
    - "learning to reweight examples for robust deep learning"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-5 (induced by source paper "submodular batch selection for training deep neural networks"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "self paced learning with adaptive deep visual embeddings"
    - "determinantal point processes for mini batch diversification"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "online batch selection for faster training of neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "autoassist a framework to accelerate training of deep neural networks"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "attention is all you need"
    - "emnist an extension of mnist to handwritten letters"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "multi30k multilingual english german image descriptions"
    - "deep residual learning for image recognition"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

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)