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: "accurate and conservative estimates of mrf log likelihood using reverse annealing"
  Abstract: Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to overestimate the log-likelihood with little indication that anything is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower bound on the log-likelihood of an approximation to the original MRF model. RAISE requires only the same MCMC transition operators as standard AIS. Experimental results indicate that RAISE agrees closely with AIS log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the side of underestimating, rather than overestimating, the log-likelihood.

Connected through hyperedge HE-1 (induced by source paper "implicit generation and generalization in energy based models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "maximum entropy generators for energy based models"
    - "the gan landscape losses architectures regularization and normalization"
    - "learning dexterous in hand manipulation"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "learning intrinsic image decomposition from watching the world"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-2 (induced by source paper "filtering variational objectives"):
  with members:
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "density estimation using real nvp"
    - "variational inference for monte carlo objectives"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "on the quantitative analysis of decoder based generative models"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-3 (induced by source paper "quantifying the probable approximation error of probabilistic inference programs"):
  with members:
    - "crosscat a fully bayesian nonparametric method for analyzing heterogeneous high dimensional data"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-4 (induced by source paper "importance weighted autoencoders"):
  with members:
    - "deep convolutional inverse graphics network"
    - "generative moment matching networks"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "semi supervised learning with deep generative models"
    - "multiple object recognition with visual attention"

Connected through hyperedge HE-5 (induced by source paper "deep unsupervised learning using nonequilibrium thermodynamics"):
  with members:
    - "nice non linear independent components estimation"
    - "reweighted wake sleep"
    - "loopy belief propagation for approximate inference an empirical study"
    - "deep generative stochastic networks trainable by backprop"
    - "fast large scale optimization by unifying stochastic gradient and quasi newton methods"
    - "deep autoregressive networks"
    - "better mixing via deep representations"
    - "fast inference in sparse coding algorithms with applications to object recognition"

Connected through hyperedge HE-6 (induced by source paper "bidirectional helmholtz machines"):
  with members:
    - "importance weighted autoencoders"
    - "blocks and fuel frameworks for deep learning"
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "iterative refinement of approximate posterior for training directed belief networks"
    - "adam a method for stochastic optimization"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "unit tests for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "cascading denoising auto encoder as a deep directed generative model"):
  with members:
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "semi supervised learning with deep generative models"
    - "generalized denoising auto encoders as generative models"

Connected through hyperedge HE-8 (induced by source paper "empirical analysis of sampling based estimators for evaluating rbms"):
  with members:
    - "bounding the test log likelihood of generative models"

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)