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: "bounding the test log likelihood of generative models"
  Abstract: Several interesting generative learning algorithms involve a complex probability distribution over many random variables, involving intractable normalization constants or latent variable normalization. Some of them may even not have an analytic expression for the unnormalized probability function and no tractable approximation. This makes it difficult to estimate the quality of these models, once they have been trained, or to monitor their quality (e.g. for early stopping) while training. A previously proposed method is based on constructing a non-parametric density estimator of the model's probability function from samples generated by the model. We revisit this idea, propose a more efficient estimator, and prove that it provides a lower bound on the true test log-likelihood, and an unbiased estimator as the number of generated samples goes to infinity, although one that incorporates the effect of poor mixing. We further propose a biased variant of the estimator that can be used reliably with a finite number of samples for the purpose of model comparison.

Connected through hyperedge HE-1 (induced by source paper "on the quantitative analysis of decoder based generative models"):
  with members:
    - "density estimation using real nvp"
    - "generating images with recurrent adversarial networks"
    - "improved techniques for training gans"
    - "measuring the reliability of mcmc inference with bidirectional monte carlo"
    - "theano a python framework for fast computation of mathematical expressions"
    - "generative visual manipulation on the natural image manifold"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generative moment matching networks"

Connected through hyperedge HE-2 (induced by source paper "gsns generative stochastic networks"):
  with members:
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "on invariance and selectivity in representation learning"
    - "deep haar scattering networks"
    - "nice non linear independent components estimation"
    - "how transferable are features in deep neural networks"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-3 (induced by source paper "latent regression bayesian network for data representation"):
  with members:
    - "neural variational inference and learning in belief networks"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-4 (induced by source paper "empirical analysis of sampling based estimators for evaluating rbms"):
  with members:
    - "accurate and conservative estimates of mrf log likelihood using reverse annealing"

Connected through hyperedge HE-5 (induced by source paper "accurate and conservative estimates of mrf log likelihood using reverse annealing"):
  with members:
    - "neural variational inference and learning in belief networks"
    - "deep autoregressive networks"
    - "sum product networks a new deep architecture"
    - "studies in lower bounding probabilities of evidence using the markov inequality"

Connected through hyperedge HE-6 (induced by source paper "understanding minimum probability flow for rbms under various kinds of dynamics"):
  with members:
    - "theano new features and speed improvements"

Connected through hyperedge HE-7 (induced by source paper "multimodal transitions for generative stochastic networks"):
  with members:
    - "deep generative stochastic networks trainable by backprop"
    - "generalized denoising auto encoders as generative models"
    - "what regularized auto encoders learn from the data generating distribution"
    - "better mixing via deep representations"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-8 (induced by source paper "generalized denoising auto encoders as generative models"):
  with members:
    - "deep generative stochastic networks trainable by backprop"
    - "a generative process for sampling contractive auto encoders"
    - "what regularized auto encoders learn from the data generating distribution"
    - "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)