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: "training simplification and model simplification for deep learning a minimal effort back propagation method"
  Abstract: We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-  $k$    k      elements (in terms of magnitude) are kept. As a result, only   $k$    k      rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of the time we only need to update fewer than 5 percent of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.

Connected through hyperedge HE-1 (induced by source paper "accelerated cnn training through gradient approximation"):
  with members:
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "nvidia tensor core programmability performance precision"
    - "faster neural network training with approximate tensor operations"
    - "sbnet sparse blocks network for fast inference"
    - "dropback continuous pruning during training"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-2 (induced by source paper "memorized sparse backpropagation"):
  with members:
    - "sparsified sgd with memory"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "minimal effort back propagation for convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"
    - "fractional max pooling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "faster neural network training with approximate tensor operations"):
  with members:
    - "factorization tricks for lstm networks"
    - "mixed precision training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "learning compact recurrent neural networks"
    - "wide residual networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "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)