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: "implicit density estimation by local moment matching to sample from auto encoders"
  Abstract: Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of the unknown data generating density. This paper contributes to the mathematical understanding of this phenomenon and helps define better justified sampling algorithms for deep learning based on auto-encoder variants. We consider an MCMC where each step samples from a Gaussian whose mean and covariance matrix depend on the previous state, defines through its asymptotic distribution a target density. First, we show that good choices (in the sense of consistency) for these mean and covariance functions are the local expected value and local covariance under that target density. Then we show that an auto-encoder with a contractive penalty captures estimators of these local moments in its reconstruction function and its Jacobian. A contribution of this work is thus a novel alternative to maximum-likelihood density estimation, which we call local moment matching. It also justifies a recently proposed sampling algorithm for the Contractive Auto-Encoder and extends it to the Denoising Auto-Encoder.

Connected through hyperedge HE-1 (induced by source paper "deep learning of representations looking forward"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"
    - "knowledge matters importance of prior information for optimization"
    - "big neural networks waste capacity"
    - "deep generative stochastic networks trainable by backprop"
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-2 (induced by source paper "representation learning a review and new perspectives"):
  with members:
    - "revisiting natural gradient for deep networks"
    - "a generative process for sampling contractive auto encoders"
    - "what regularized auto encoders learn from the data generating distribution"
    - "practical recommendations for gradient based training of deep architectures"
    - "better mixing via deep representations"
    - "multi column deep neural networks for image classification"
    - "shift invariance sparse coding for audio classification"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "practical recommendations for gradient based training of deep architectures"):
  with members:
    - "learning internal representations"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "a generative process for sampling contractive auto encoders"
    - "natural language processing almost from scratch"
    - "from machine learning to machine reasoning"

Connected through hyperedge HE-4 (induced by source paper "what regularized auto encoders learn from the data generating distribution"):
  with members:
    - "generalized denoising auto encoders as generative models"
    - "a generative process for sampling contractive auto encoders"
    - "representation learning a review and new perspectives"
    - "better mixing via deep representations"

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)