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: "pushing stochastic gradient towards second order methods backpropagation learning with transformations in nonlinearities"
  Abstract: Recently, we proposed to transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. We continue the work by firstly introducing a third transformation to normalize the scale of the outputs of each hidden neuron, and secondly by analyzing the connections to second order optimization methods. We show that the transformations make a simple stochastic gradient behave closer to second-order optimization methods and thus speed up learning. This is shown both in theory and with experiments. The experiments on the third transformation show that while it further increases the speed of learning, it can also hurt performance by converging to a worse local optimum, where both the inputs and outputs of many hidden neurons are close to zero.

Connected through hyperedge HE-1 (induced by source paper "deep residual learning for image recognition"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "object detection networks on convolutional feature maps"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "delving deep into rectifiers surpassing human level performance on imagenet classification"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "one weird trick for parallelizing convolutional neural networks"
    - "convolutional neural networks at constrained time cost"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-3 (induced by source paper "optimizing neural networks with kronecker factored approximate curvature"):
  with members:
    - "on the expressive efficiency of sum product networks"
    - "parallel training of dnns with natural gradient and parameter averaging"
    - "new insights and perspectives on the natural gradient method"
    - "training neural networks with stochastic hessian free optimization"
    - "riemannian metrics for neural networks i feedforward networks"
    - "revisiting natural gradient for deep networks"
    - "hybrid deterministic stochastic methods for data fitting"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "scalable gradient based tuning of continuous regularization hyperparameters"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "semi supervised learning with ladder networks"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"
    - "theano new features and speed improvements"

Connected through hyperedge HE-5 (induced by source paper "knowledge matters importance of prior information for optimization"):
  with members:
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "big neural networks waste capacity"
    - "a generative process for sampling contractive auto encoders"
    - "practical recommendations for gradient based training of deep architectures"
    - "representation learning a review and new perspectives"
    - "evolving culture vs local minima"
    - "adadelta an adaptive learning rate method"

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)