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: "understanding the disharmony between dropout and batch normalization by variance shift"
  Abstract: This paper first answers the question "why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined together?" in both theoretical and statistical aspects. Theoretically, we find that Dropout would shift the variance of a specific neural unit when we transfer the state of that network from train to test. However, BN would maintain its statistical variance, which is accumulated from the entire learning procedure, in the test phase. The inconsistency of that variance (we name this scheme as "variance shift") causes the unstable numerical behavior in inference that leads to more erroneous predictions finally, when applying Dropout before BN. Thorough experiments on DenseNet, ResNet, ResNeXt and Wide ResNet confirm our findings. According to the uncovered mechanism, we next explore several strategies that modifies Dropout and try to overcome the limitations of their combination by avoiding the variance shift risks.

Connected through hyperedge HE-1 (induced by source paper "salsanext fast semantic segmentation of lidar point clouds for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "deep learning for 3d point clouds a survey"
    - "salsanet fast road and vehicle segmentation in lidar point clouds for autonomous driving"
    - "fast scnn fast semantic segmentation network"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"

Connected through hyperedge HE-2 (induced by source paper "exploiting the full capacity of deep neural networks while avoiding overfitting by targeted sparsity regularization"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "on the convergence of adam and beyond"
    - "adam induces implicit weight sparsity in rectifier neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mixup as locally linear out of manifold regularization"
    - "on implicit filter level sparsity in convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier"):
  with members:
    - "deep learning for time series classification a review"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "text based lstm networks for automatic music composition"
    - "deepid3 face recognition with very deep neural networks"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "al2 progressive activation loss for learning general representations in classification neural networks"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "do imagenet classifiers generalize to imagenet"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dimensionality driven learning with noisy labels"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "exploring generalization in deep learning"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-5 (induced by source paper "albert a lite bert for self supervised learning of language representations"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "spanbert improving pre training by representing and predicting spans"
    - "well read students learn better on the importance of pre training compact models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "structbert incorporating language structures into pre training for deep language understanding"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-6 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-7 (induced by source paper "ensemble model patching a parameter efficient variational bayesian neural network"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "an empirical model of large batch training"
    - "adversarial distillation of bayesian neural network posteriors"
    - "a style based generator architecture for generative adversarial networks"
    - "averaging weights leads to wider optima and better generalization"
    - "k for the price of 1 parameter efficient multi task and transfer learning"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-8 (induced by source paper "effective and efficient dropout for deep convolutional neural networks"):
  with members:
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "group normalization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "noscope optimizing neural network queries over video at scale"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)