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: "conjugate computation variational inference converting variational inference in non conjugate models to inferences in conjugate models"
  Abstract: Variational inference is computationally challenging in models that contain both conjugate and non-conjugate terms. Methods specifically designed for conjugate models, even though computationally efficient, find it difficult to deal with non-conjugate terms. On the other hand, stochastic-gradient methods can handle the non-conjugate terms but they usually ignore the conjugate structure of the model which might result in slow convergence. In this paper, we propose a new algorithm called Conjugate-computation Variational Inference (CVI) which brings the best of the two worlds together -- it uses conjugate computations for the conjugate terms and employs stochastic gradients for the rest. We derive this algorithm by using a stochastic mirror-descent method in the mean-parameter space, and then expressing each gradient step as a variational inference in a conjugate model. We demonstrate our algorithm's applicability to a large class of models and establish its convergence. Our experimental results show that our method converges much faster than the methods that ignore the conjugate structure of the model.

Connected through hyperedge HE-1 (induced by source paper "training binary neural networks using the bayesian learning rule"):
  with members:
    - "meliusnet can binary neural networks achieve mobilenet level accuracy"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "mirror descent view for neural network quantization"
    - "latent weights do not exist rethinking binarized neural network optimization"
    - "proximal mean field for neural network quantization"
    - "wrpn wide reduced precision networks"
    - "towards accurate binary convolutional neural network"
    - "learning discrete weights using the local reparameterization trick"

Connected through hyperedge HE-2 (induced by source paper "stochastic implicit natural gradient for black box optimization"):
  with members:
    - "diagonal acceleration for covariance matrix adaptation evolution strategies"
    - "provable smoothness guarantees for black box variational inference"
    - "efficient batch black box optimization with deterministic regret bounds"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-3 (induced by source paper "provable smoothness guarantees for black box variational inference"):
  with members:
    - "fast black box variational inference through stochastic trust region optimization"
    - "boosting variational inference an optimization perspective"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "variational inference in pachinko allocation machines"):
  with members:
    - "neural discrete representation learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "nonparametric bayes pachinko allocation"

Connected through hyperedge HE-5 (induced by source paper "slang fast structured covariance approximations for bayesian deep learning with natural gradient"):
  with members:
    - "noisy natural gradient as variational inference"
    - "adam a method for stochastic optimization"
    - "new perspectives on the natural gradient method"

Connected through hyperedge HE-6 (induced by source paper "topic modeling on health journals with regularized variational inference"):
  with members:
    - "continuous time dynamic topic models"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-7 (induced by source paper "stochastic sequential neural networks with structured inference"):
  with members:
    - "stochastic generative hashing"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "theano a python framework for fast computation of mathematical expressions"
    - "hierarchical multiscale recurrent neural networks"
    - "blocks and fuel frameworks for deep learning"
    - "a recurrent latent variable model for sequential data"
    - "muprop unbiased backpropagation for stochastic neural networks"

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)