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: "topic guided variational autoencoders for text generation"
  Abstract: We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Gaussian prior for the latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each mixture component corresponds to a latent topic, which provides guidance to generate sentences under the topic. The neural topic module and the VAE-based neural sequence module in our model are learned jointly. In particular, a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during model inference. Experimental results show that our TGVAE outperforms alternative approaches on both unconditional and conditional text generation, which can generate semantically-meaningful sentences with various topics.

Connected through hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"
    - "emotionlines an emotion corpus of multi party conversations"

Connected through hyperedge HE-3 (induced by source paper "an optimal transport framework for zero shot learning"):
  with members:
    - "improving textual network embedding with global attention via optimal transport"
    - "improving textual network learning with variational homophilic embeddings"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "zero shot kernel learning"
    - "improving gans using optimal transport"

Connected through hyperedge HE-4 (induced by source paper "towards generating long and coherent text with multi level latent variable models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "variational sequential labelers for semi supervised learning"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "a hierarchical latent structure for variational conversation modeling"

Connected through hyperedge HE-5 (induced by source paper "fixing gaussian mixture vaes for interpretable text generation"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "spherical latent spaces for stable variational autoencoders"
    - "toward controlled generation of text"
    - "key value retrieval networks for task oriented dialogue"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "latent intention dialogue models"
    - "deep recurrent generative decoder for abstractive text summarization"

Connected through hyperedge HE-6 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "pytorch biggraph a large scale graph embedding system"
    - "network structure from rich but noisy data"
    - "understanding convolutional neural networks for text classification"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "interpretable vaes for nonlinear group factor analysis"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (induced by source paper "collaborative filtering with a synthetic feedback loop"):
  with members:
    - "improving textual network learning with variational homophilic embeddings"
    - "neurec on nonlinear transformation for personalized ranking"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "wasserstein autoencoders for collaborative filtering"
    - "outer product based neural collaborative filtering"
    - "top k off policy correction for a reinforce recommender system"
    - "zero shot learning via class conditioned deep generative models"
    - "neural collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "recurrent hierarchical topic guided neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "bottom up abstractive summarization"
    - "dirichlet belief networks for topic structure learning"
    - "topic compositional neural language model"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "topically driven neural language model"
    - "discovering discrete latent topics with neural variational inference"

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)