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: "dirichlet variational autoencoder for text modeling"
  Abstract: We introduce an improved variational autoencoder (VAE) for text modeling with topic information explicitly modeled as a Dirichlet latent variable. By providing the proposed model topic awareness, it is more superior at reconstructing input texts. Furthermore, due to the inherent interactions between the newly introduced Dirichlet variable and the conventional multivariate Gaussian variable, the model is less prone to KL divergence vanishing. We derive the variational lower bound for the new model and conduct experiments on four different data sets. The results show that the proposed model is superior at text reconstruction across the latent space and classifications on learned representations have higher test accuracies.

Connected through hyperedge HE-1 (induced by source paper "text modeling with syntax aware variational autoencoders"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "variational knowledge graph reasoning"
    - "syntax directed variational autoencoder for structured data"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-2 (induced by source paper "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "spherical latent spaces for stable variational autoencoders"
    - "auto encoding total correlation explanation"
    - "style transfer from non parallel text by cross alignment"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "latent space factorisation and manipulation via matrix subspace projection"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "deep nets what have they ever done for vision"
    - "learning latent subspaces in variational autoencoders"
    - "i revnet deep invertible networks"
    - "toward controlled generation of text"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-4 (induced by source paper "neural gaussian copula for variational autoencoder"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "style transfer from non parallel text by cross alignment"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "cached long short term memory neural networks for document level sentiment classification"
    - "generating sentences from a continuous space"

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.CL(Computation and Language)