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 neural networks with stochastic hessian free optimization"
  Abstract: Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property and study stochastic HF with gradient and curvature mini-batches independent of the dataset size. We modify Martens' HF for these settings and integrate dropout, a method for preventing co-adaptation of feature detectors, to guard against overfitting. Stochastic Hessian-free optimization gives an intermediary between SGD and HF that achieves competitive performance on both classification and deep autoencoder experiments.

Connected through hyperedge HE-1 (induced by source paper "efficient subsampled gauss newton and natural gradient methods for training neural networks"):
  with members:
    - "a gram gauss newton method learning overparameterized deep neural networks for regression problems"
    - "improving generalization performance by switching from adam to sgd"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "block diagonal hessian free optimization for training neural networks"):
  with members:
    - "sharp minima can generalize for deep nets"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "distributed hessian free optimization for deep neural network"
    - "theano a python framework for fast computation of mathematical expressions"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-3 (induced by source paper "distributed hessian free optimization for deep neural network"):
  with members:
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "mini batch primal and dual methods for svms"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-4 (induced by source paper "large scale distributed hessian free optimization for deep neural network"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "very deep convolutional networks for large scale image recognition"
    - "mini batch primal and dual methods for svms"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-5 (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"
    - "riemannian metrics for neural networks i feedforward networks"
    - "revisiting natural gradient for deep networks"
    - "pushing stochastic gradient towards second order methods backpropagation learning with transformations in nonlinearities"
    - "hybrid deterministic stochastic methods for data fitting"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "fast and accurate deep network learning by exponential linear units elus"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "striving for simplicity the all convolutional net"
    - "fractional max pooling"
    - "riemannian metrics for neural networks i feedforward networks"
    - "network in network"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-7 (induced by source paper "revisiting natural gradient for deep networks"):
  with members:
    - "metric free natural gradient for joint training of boltzmann machines"
    - "the natural gradient by analogy to signal whitening and recipes and tricks for its use"
    - "theano new features and speed improvements"

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)