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: "metric free natural gradient for joint training of boltzmann machines"
  Abstract: This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm belongs to the family of truncated Newton methods and exploits an efficient matrix-vector product to avoid explicitely storing the natural gradient metric $L$. This metric is shown to be the expected second derivative of the log-partition function (under the model distribution), or equivalently, the variance of the vector of partial derivatives of the energy function. We evaluate our method on the task of joint-training a 3-layer Deep Boltzmann Machine and show that MFNG does indeed have faster per-epoch convergence compared to Stochastic Maximum Likelihood with centering, though wall-clock performance is currently not competitive.

Connected through hyperedge HE-1 (induced by source paper "efficient learning of restricted boltzmann machines using covariance estimates"):
  with members:
    - "restricted boltzmann machines introduction and review"
    - "learning rbm with a dc programming approach"

Connected through hyperedge HE-2 (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"
    - "training neural networks with stochastic hessian free optimization"
    - "riemannian metrics for neural networks i feedforward networks"
    - "network in network"

Connected through hyperedge HE-3 (induced by source paper "revisiting natural gradient for deep networks"):
  with members:
    - "training neural networks with stochastic hessian free optimization"
    - "the natural gradient by analogy to signal whitening and recipes and tricks for its use"
    - "theano new features and speed improvements"

Connected through hyperedge HE-4 (induced by source paper "understanding boltzmann machine and deep learning via a confident information first principle"):
  with members:
    - "big neural networks waste capacity"
    - "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)