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: "not all samples are created equal deep learning with importance sampling"
  Abstract: Deep neural network training spends most of the computation on examples that are properly handled, and could be ignored. #R##N#We propose to mitigate this phenomenon with a principled importance sampling scheme that focuses computation on "informative" examples, and reduces the variance of the stochastic gradients during training. Our contribution is twofold: first, we derive a tractable upper bound to the per-sample gradient norm, and second we derive an estimator of the variance reduction achieved with importance sampling, which enables us to switch it on when it will result in an actual speedup. #R##N#The resulting scheme can be used by changing a few lines of code in a standard SGD procedure, and we demonstrate experimentally, on image classification, CNN fine-tuning, and RNN training, that for a fixed wall-clock time budget, it provides a reduction of the train losses of up to an order of magnitude and a relative improvement of test errors between 5% and 17%.

Connected through hyperedge HE-1 (induced by source paper "no regret sample selection with noisy labels"):
  with members:
    - "introduction to online convex optimization"
    - "nlnl negative learning for noisy labels"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "joint optimization framework for learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "a semi supervised two stage approach to learning from noisy labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-2 (induced by source paper "investigating the compositional structure of deep neural networks"):
  with members:
    - "deep neural network ensembles"
    - "an empirical study of example forgetting during deep neural network learning"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning what data to learn"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "understanding deep learning requires rethinking generalization"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-3 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets"):
  with members:
    - "safe adaptive importance sampling"
    - "biased importance sampling for deep neural network training"
    - "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-4 (induced by source paper "dice loss for data imbalanced nlp tasks"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "libra r cnn towards balanced learning for object detection"
    - "glyce glyph vectors for chinese character representations"
    - "towards accurate one stage object detection with ap loss"
    - "dsreg using distant supervision as a regularizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "chinese ner using lattice lstm"

Connected through hyperedge HE-5 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-6 (induced by source paper "in defense of the triplet loss again learning robust person re identification with fast approximated triplet loss and label distillation"):
  with members:
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "calibrated domain invariant learning for highly generalizable large scale re identification"
    - "the devil of face recognition is in the noise"
    - "support neighbor loss for person re identification"
    - "modality distillation with multiple stream networks for action recognition"
    - "iterative learning with open set noisy labels"
    - "in defense of the triplet loss for person re identification"
    - "margin sample mining loss a deep learning based method for person re identification"

Connected through hyperedge HE-7 (induced by source paper "mining gold samples for conditional gans"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "variational adversarial active learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "discriminative active learning"

Connected through hyperedge HE-8 (induced by source paper "learning data manipulation for augmentation and weighting"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "on learning intrinsic rewards for policy gradient methods"
    - "autoaugment learning augmentation policies from data"
    - "conditional bert contextual augmentation"
    - "bert pre training of deep bidirectional transformers for language 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)