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 the generalization error bounds of neural networks under diversity inducing mutual angular regularization"
  Abstract: Recently diversity-inducing regularization methods for latent variable models (LVMs), which encourage the components in LVMs to be diverse, have been studied to address several issues involved in latent variable modeling: (1) how to capture long-tail patterns underlying data; (2) how to reduce model complexity without sacrificing expressivity; (3) how to improve the interpretability of learned patterns. While the effectiveness of diversity-inducing regularizers such as the mutual angular regularizer has been demonstrated empirically, a rigorous theoretical analysis of them is still missing. In this paper, we aim to bridge this gap and analyze how the mutual angular regularizer (MAR) affects the generalization performance of supervised LVMs. We use neural network (NN) as a model instance to carry out the study and the analysis shows that increasing the diversity of hidden units in NN would reduce estimation error and increase approximation error. In addition to theoretical analysis, we also present empirical study which demonstrates that the MAR can greatly improve the performance of NN and the empirical observations are in accordance with the theoretical analysis.

Connected through hyperedge HE-1 (induced by source paper "on correlation of features extracted by deep neural networks"):
  with members:
    - "diversity regularized adversarial learning"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "building efficient convnets using redundant feature pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "self normalizing neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "learning structured sparsity in deep neural networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "diversity regularized adversarial learning"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "building efficient convnets using redundant feature pruning"
    - "stabilizing training of generative adversarial networks through regularization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-3 (induced by source paper "understanding generalization through visualizations"):
  with members:
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "averaging weights leads to wider optima and better generalization"
    - "certified defenses for data poisoning attacks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "data dependent stability of stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"

Connected through hyperedge HE-5 (induced by source paper "two dimensional stochastic configuration networks for image data analytics"):
  with members:
    - "learning less overlapping representations"
    - "recovery guarantees for one hidden layer neural networks"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "deep stochastic configuration networks with universal approximation property"
    - "matrix neural networks"
    - "approximation with random bases"
    - "matrix variate rbm model with gaussian distributions"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "radius margin bounds for deep neural networks"):
  with members:
    - "identity mappings in deep residual networks"
    - "benefits of depth in neural networks"
    - "wide residual networks"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "on the depth of deep neural networks a theoretical view"
    - "on the expressive power of deep learning a tensor analysis"
    - "robustness and generalization"

Connected through hyperedge HE-7 (induced by source paper "task specific attentive pooling of phrase alignments contributes to sentence matching"):
  with members:
    - "attentive pooling networks"
    - "simple question answering by attentive convolutional neural network"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "applying deep learning to answer selection a study and an open task"
    - "effective approaches to attention based neural machine translation"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-8 (induced by source paper "deep semi random features for nonlinear function approximation"):
  with members:
    - "diverse neural network learns true target functions"
    - "distribution specific hardness of learning neural networks"
    - "norm based capacity control in neural networks"
    - "on the depth of deep neural networks a theoretical view"
    - "the loss surfaces of multilayer networks"
    - "the arcade learning environment an evaluation platform for general agents"

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)