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: "improving deep learning using generic data augmentation"
  Abstract: Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious. Data augmentation overcomes this issue by artificially inflating the training set with label preserving transformations. Recently there has been extensive use of generic data augmentation to improve Convolutional Neural Network (CNN) task performance. This study benchmarks various popular data augmentation schemes to allow researchers to make informed decisions as to which training methods are most appropriate for their data sets. Various geometric and photometric schemes are evaluated on a coarse-grained data set using a relatively simple CNN. Experimental results, run using 4-fold cross-validation and reported in terms of Top-1 and Top-5 accuracy, indicate that cropping in geometric augmentation significantly increases CNN task performance.

Connected through hyperedge HE-1 (induced by source paper "model assertions for monitoring and improving ml model"):
  with members:
    - "better ai through logical scaffolding"
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "outside the box abstraction based monitoring of neural networks"
    - "scaling video analytics on constrained edge nodes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vstore a data store for analytics on large videos"
    - "selection via proxy efficient data selection for deep learning"
    - "formal security analysis of neural networks using symbolic intervals"

Connected through hyperedge HE-2 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "safe augmentation learning task specific transformations from data"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-4 (induced by source paper "kekulescope prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images"):
  with members:
    - "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"
    - "hydranet data augmentation for regression neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "molecular de novo design through deep reinforcement learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "moleculenet a benchmark for molecular machine learning"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"

Connected through hyperedge HE-5 (induced by source paper "achieving generalizable robustness of deep neural networks by stability training"):
  with members:
    - "learning optimal data augmentation policies via bayesian optimization for image classification tasks"
    - "unsupervised data augmentation for consistency training"
    - "does data augmentation lead to positive margin"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"
    - "methods for interpreting and understanding deep neural networks"
    - "autoaugment learning augmentation policies from data"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "data augmentation for deep transfer learning"):
  with members:
    - "when does label smoothing help"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "data interpolating prediction alternative interpretation of mixup"):
  with members:
    - "data augmentation by pairing samples for images classification"
    - "mixup as locally linear out of manifold regularization"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"
    - "between class learning for image classification"
    - "identity mappings in deep residual networks"
    - "importance weighted autoencoders"
    - "apac augmented pattern classification with neural networks"

Connected through hyperedge HE-8 (induced by source paper "a preliminary study on data augmentation of deep learning for image classification"):
  with members:
    - "deepgini prioritizing massive tests to reduce labeling cost"
    - "neuralvis visualizing and interpreting deep learning models"
    - "autoaugment learning augmentation policies from data"
    - "recent advances in recurrent neural networks"
    - "random erasing data augmentation"
    - "improved regularization of convolutional neural networks with cutout"
    - "regularization for unsupervised deep neural nets"

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)