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: "variational attention for sequence to sequence models"
  Abstract: The variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network. In natural language processing, sequence-to-sequence (Seq2Seq) models typically serve as encoder-decoder networks. When combined with a traditional (deterministic) attention mechanism, the variational latent space may be bypassed by the attention model, making the generated sentences less diversified. In our paper, we propose a variational attention mechanism for VED, where the attention vector is modeled as normally distributed random variables. Experiments show that variational attention increases diversity while retaining high quality. We also show that the model is not sensitive to hyperparameters.

Connected through hyperedge HE-1 (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"
    - "topic guided variational autoencoders for text generation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "knowledge grounded response generation with deep attentional latent variable model"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "understanding convolutional neural networks for text classification"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "neural approaches to conversational ai"
    - "variational memory encoder decoder"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "language coverage and generalization in rnn based continuous sentence embeddings for interacting agents"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "semi parametric topological memory for navigation"
    - "chalet cornell house agent learning environment"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "gated attention architectures for task oriented language grounding"

Connected through hyperedge HE-4 (induced by source paper "conditional response generation using variational alignment"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "a hierarchical latent structure for variational conversation modeling"
    - "improving variational encoder decoders in dialogue generation"
    - "style transfer from non parallel text by cross alignment"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "stylized text generation using wasserstein autoencoders with a mixture of gaussian prior"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "topic guided variational autoencoders for text generation"
    - "structuring latent spaces for stylized response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "disentangled representation learning for non parallel text style transfer"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-7 (induced by source paper "disentangled representation learning for non parallel text style transfer"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "deep photo style transfer"
    - "style transfer in text exploration and evaluation"
    - "disentangling factors of variation in deep representations using adversarial training"

Connected through hyperedge HE-8 (induced by source paper "amortized context vector inference for sequence to sequence networks"):
  with members:
    - "structured attention networks"
    - "attention is all you need"
    - "efficient summarization with read again and copy mechanism"
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "modeling coverage for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

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)