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: "variational auto encoded deep gaussian processes"
  Abstract: We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.

Connected through hyperedge HE-1 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-2 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-3 (induced by source paper "deep probabilistic kernels for sample efficient learning"):
  with members:
    - "sampled softmax with random fourier features"
    - "a closer look at few shot classification"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-4 (induced by source paper "implicit posterior variational inference for deep gaussian processes"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "collective online learning of gaussian processes in massive multi agent systems"
    - "adversarial distillation of bayesian neural network posteriors"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational inference for bayesian sparse gaussian process regression"
    - "wavenet a generative model for raw audio"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "probabilistic modeling for novelty detection with applications to fraud identification"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "a comparative evaluation of novelty detection algorithms for discrete sequences"
    - "visual interpretability for deep learning a survey"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "neural autoregressive distribution estimation"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "density estimation using real nvp"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "harmonized multimodal learning with gaussian process latent variable models"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "deep multimodal representation learning from temporal data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "training and evaluating multimodal word embeddings with large scale web annotated images"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-7 (induced by source paper "on the expected behaviour of noise regularised deep neural networks as gaussian processes"):
  with members:
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "learning scalable deep kernels with recurrent structure"
    - "steps toward deep kernel methods from infinite neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "efficient deep gaussian process models for variable sized input"):
  with members:
    - "efficient approximation algorithms for string kernel based sequence classification"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "adam a method for stochastic optimization"

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)