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: "fast gradient based inference with continuous latent variable models in auxiliary form"
  Abstract: We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent vari- ables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally deterministic given their parents and a set of independent auxiliary variables. Variables of mod- els in this auxiliary form have much larger Markov blankets, leading to significant speedups in gradient-based inference, e.g. rapid mixing Hybrid Monte Carlo and efficient gradient-based optimization. The relative efficiency is confirmed in ex- periments.

Connected through hyperedge HE-1 (induced by source paper "energy based models for video anomaly detection"):
  with members:
    - "learning temporal regularity in video sequences"
    - "importance weighted autoencoders"
    - "a recurrent latent variable model for sequential data"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "conditional generative adversarial nets"
    - "learning to generate chairs with convolutional neural networks"
    - "conditional restricted boltzmann machines for structured output prediction"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-2 (induced by source paper "nips 2016 tutorial generative adversarial networks"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"
    - "neural photo editing with introspective adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "density estimation using real nvp"
    - "generative adversarial imitation learning"
    - "connecting generative adversarial networks and actor critic methods"

Connected through hyperedge HE-3 (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-4 (induced by source paper "efficient gradient based inference through transformations between bayes nets and neural nets"):
  with members:
    - "deep generative stochastic networks trainable by backprop"
    - "estimating or propagating gradients through stochastic neurons"
    - "expectation propagation for approximate bayesian inference"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "deep generative stochastic networks trainable by backprop"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons"
    - "a generative process for sampling contractive auto encoders"
    - "what regularized auto encoders learn from the data generating distribution"
    - "sum product networks a new deep architecture"
    - "texture modeling with convolutional spike and slab rbms and deep extensions"
    - "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)