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: "on correlation of features extracted by deep neural networks"
  Abstract: Redundancy in deep neural network (DNN) models has always been one of their most intriguing and important properties. DNNs have been shown to overparameterize, or extract a lot of redundant features. In this work, we explore the impact of size (both width and depth), activation function, and weight initialization on the susceptibility of deep neural network models to extract redundant features. To estimate the number of redundant features in each layer, all the features of a given layer are hierarchically clustered according to their relative cosine distances in feature space and a set threshold. It is shown that both network size and activation function are the two most important components that foster the tendency of DNNs to extract redundant features. The concept is illustrated using deep multilayer perceptron and convolutional neural networks on MNIST digits recognition and CIFAR-10 dataset, respectively.

Connected through hyperedge HE-1 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification"):
  with members:
    - "a simple joint model for improved contextual neural lemmatization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "word embeddings a survey"
    - "a robust hybrid approach for textual document classification"
    - "character n gram embeddings to improve rnn language models"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"

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 the convergence of adam and beyond"
    - "adam induces implicit weight sparsity in rectifier neural networks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "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 "a robust hybrid approach for textual document classification"):
  with members:
    - "on the role of text preprocessing in neural network architectures an evaluation study on text categorization and sentiment analysis"
    - "advances in pre training distributed word representations"
    - "going deeper with convolutions"
    - "distributed representations of sentences and documents"
    - "predicting parameters in deep learning"
    - "distributed representations of words and phrases and their compositionality"

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)