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: "deep learning of constrained autoencoders for enhanced understanding of data"
  Abstract: Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand them, however, remain very limited. This is especially prominent when multilayer deep learning architectures are used. This paper demonstrates how to remove these bottlenecks within the architecture of non-negativity constrained autoencoder. It is shown that using both L1 and L2 regularizations that induce non-negativity of weights, most of the weights in the network become constrained to be non-negative, thereby resulting into a more understandable structure with minute deterioration in classification accuracy. Also, this proposed approach extracts features that are more sparse and produces additional output layer sparsification. The method is analyzed for accuracy and feature interpretation on the MNIST data, the NORB normalized uniform object data, and the Reuters text categorization data set.

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (induced by source paper "on correlation of features extracted by deep neural networks"):
  with members:
    - "diversity regularized adversarial learning"
    - "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"
    - "dsd dense sparse dense training for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "diversity regularized adversarial learning"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "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"
    - "neural photo editing with introspective adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "knowledge adaptation for efficient semantic segmentation"):
  with members:
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "non local neural networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "part based approximations for morphological operators using asymmetric auto encoders"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep learning of part based representation of data using sparse autoencoders with nonnegativity constraints"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to generate chairs with convolutional neural networks"
    - "sparse modeling for image and vision processing"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "pilae a non gradient descent learning scheme for deep feedforward neural networks"):
  with members:
    - "failures of gradient based deep learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "evolutionary cost sensitive extreme learning machine"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "optimal sparse linear auto encoders and sparse pca"
    - "openml networked science in machine learning"
    - "what regularized auto encoders learn from the data generating distribution"
    - "representation learning a review and new perspectives"

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)