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: "effective estimation of deep generative language models"
  Abstract: Advances in variational inference enable parameterisation of probabilistic models by deep neural networks. This combines the statistical transparency of the probabilistic modelling framework with the representational power of deep learning. Yet, it seems difficult to effectively estimate such models in the context of language modelling. Even models based on rather simple generative stories struggle to make use of additional structure due to a problem known as posterior collapse. We concentrate on one such model, namely, a variational auto-encoder, which we argue is an important building block in hierarchical probabilistic models of language. This paper contributes a sober view of the problem, a survey of techniques to address it, novel techniques, and extensions to the model. Our experiments on modelling written English text support a number of recommendations that should help researchers interested in this exciting field.

Connected through hyperedge HE-1 (induced by source paper "on the importance of the kullback leibler divergence term in variational autoencoders for text generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "iterative amortized inference"
    - "hierarchical neural story generation"
    - "spherical latent spaces for stable variational autoencoders"
    - "targeted syntactic evaluation of language models"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "a surprisingly effective fix for deep latent variable modeling of text"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "spherical latent spaces for stable variational autoencoders"
    - "style transfer from non parallel text by cross alignment"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "adversarially regularized autoencoders"
    - "infovae information maximizing variational autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"

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)