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 weight initialization in deep neural networks"
  Abstract: A proper initialization of the weights in a neural network is critical to its convergence. Current insights into weight initialization come primarily from linear activation functions. In this paper, I develop a theory for weight initializations with non-linear activations. First, I derive a general weight initialization strategy for any neural network using activation functions differentiable at 0. Next, I derive the weight initialization strategy for the Rectified Linear Unit (RELU), and provide theoretical insights into why the Xavier initialization is a poor choice with RELU activations. My analysis provides a clear demonstration of the role of non-linearities in determining the proper weight initializations.

Connected through hyperedge HE-1 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"

Connected through hyperedge HE-2 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-4 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-5 (induced by source paper "lidar icps net indoor camera positioning based on generative adversarial network for rgb to point cloud translation"):
  with members:
    - "icps net an end to end rgb based indoor camera positioning system using deep convolutional neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "swish a self gated activation function"
    - "u net convolutional networks for biomedical image segmentation"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-6 (induced by source paper "compnet neural networks growing via the compact network morphism"):
  with members:
    - "an equivalence of fully connected layer and convolutional layer"
    - "neural optimizer search with reinforcement learning"
    - "machine learning modeling for time series problem predicting flight ticket prices"
    - "a survey of model compression and acceleration for deep neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "skip connections as effective symmetry breaking"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"

Connected through hyperedge HE-7 (induced by source paper "detecting dead weights and units in neural networks"):
  with members:
    - "model compression via distillation and quantization"
    - "attacking binarized neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "towards the limit of network quantization"
    - "learning structured sparsity in deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-8 (induced by source paper "gradient target propagation"):
  with members:
    - "biologically motivated algorithms for propagating local target representations"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"

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)