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: "nonparametric variational inference"
  Abstract: Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Using multiple kernels allows the approximation to capture multiple modes of the posterior, unlike most other variational approximations. We demonstrate the efficacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and sample-based approximations. The method is easy to apply to more general graphical models for which standard variational methods are difficult to derive.

Connected through hyperedge HE-1 (induced by source paper "lifted hybrid variational inference"):
  with members:
    - "coarse to fine lifted map inference in computer vision"
    - "lifted tree reweighted variational inference"
    - "beyond log supermodularity lower bounds and the bethe partition function"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "lifted relational variational inference"
    - "lifted inference for relational continuous models"
    - "counting belief propagation"
    - "the bethe partition function of log supermodular graphical models"

Connected through hyperedge HE-2 (induced by source paper "try depth instead of weight correlations mean field is a less restrictive assumption for deeper networks"):
  with members:
    - "functional variational bayesian neural networks"
    - "on the convergence of adam and beyond"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-3 (induced by source paper "ensemble model patching a parameter efficient variational bayesian neural network"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "an empirical model of large batch training"
    - "adversarial distillation of bayesian neural network posteriors"
    - "a style based generator architecture for generative adversarial networks"
    - "averaging weights leads to wider optima and better generalization"
    - "k for the price of 1 parameter efficient multi task and transfer learning"

Connected through hyperedge HE-4 (induced by source paper "boovae a scalable framework for continual vae learning under boosting approach"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "inference suboptimality in variational autoencoders"
    - "continual learning in generative adversarial nets"
    - "progressive neural networks"
    - "vae with a vampprior"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "meta particle flow for sequential bayesian inference"):
  with members:
    - "monge amp ere flow for generative modeling"
    - "deep sets"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "filtering variational objectives"
    - "a geometric view of optimal transportation and generative model"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-6 (induced by source paper "particle flow bayes rule"):
  with members:
    - "stable architectures for deep neural networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "filtering variational objectives"
    - "learning combinatorial optimization algorithms over graphs"
    - "learning to learn by gradient descent by gradient descent"
    - "provable bayesian inference via particle mirror descent"

Connected through hyperedge HE-7 (induced by source paper "trust region variational inference with gaussian mixture models"):
  with members:
    - "fast black box variational inference through stochastic trust region optimization"
    - "model free trajectory based policy optimization with monotonic improvement"
    - "trust region policy optimization"

Connected through hyperedge HE-8 (induced by source paper "quality of uncertainty quantification for bayesian neural network inference"):
  with members:
    - "functional variational bayesian 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)