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 auto encoders for hierarchical representation learning"
  Abstract: The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space. Both the neural parameters and Bayesian priors are learned jointly using tailored variational inference. The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances. We apply our model in video representation learning. Our method is able to discover highly interpretable activity hierarchies, and obtain improved clustering accuracy and generalization capacity based on the learned rich representations.

Connected through hyperedge HE-1 (induced by source paper "effective estimation of deep generative language models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "preventing posterior collapse with delta vaes"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "neural autoregressive flows"
    - "a hierarchical latent structure for variational conversation modeling"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "scene induced multi modal trajectory forecasting via planning"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "preventing posterior collapse with delta vaes"
    - "structured output learning with conditional generative flows"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "videoflow a flow based generative model for video"

Connected through hyperedge HE-3 (induced by source paper "on the necessity and effectiveness of learning the prior of variational auto encoder"):
  with members:
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "learnable explicit density for continuous latent space and variational inference"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "vae with a vampprior"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "density estimation using real nvp"
    - "pixelvae a latent variable model for natural images"

Connected through hyperedge HE-4 (induced by source paper "learning priors for adversarial autoencoders"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "improved training of wasserstein gans"
    - "vae with a vampprior"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-5 (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"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-6 (induced by source paper "document hashing with mixture prior generative models"):
  with members:
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "variational deep semantic hashing for text documents"
    - "a survey on learning to hash"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "variational lossy autoencoder"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-7 (induced by source paper "hierarchically clustered representation learning"):
  with members:
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "infinite variational autoencoder for semi supervised learning"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "representation learning a review and new perspectives"
    - "kernel topic models"

Connected through hyperedge HE-8 (induced by source paper "auto encoding variational neural machine translation"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "spherical latent spaces for stable variational autoencoders"
    - "a call for clarity in reporting bleu scores"
    - "joint training for neural machine translation models with monolingual data"
    - "explaining and generalizing back translation through wake sleep"
    - "generative neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "fixing a broken elbo"

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)