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: "natural language generation for spoken dialogue system using rnn encoder decoder networks"
  Abstract: Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets.

Connected through hyperedge HE-1 (induced by source paper "few shot natural language generation for task oriented dialog"):
  with members:
    - "towards a human like open domain chatbot"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "plug and play language models a simple approach to controlled text generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "defending against neural fake news"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "dialogpt large scale generative pre training for conversational response generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "stochastic natural language generation using dependency information"):
  with members:
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "findings of the e2e nlg challenge"
    - "order planning neural text generation from structured data"
    - "the e2e dataset new challenges for end to end generation"
    - "table to text generation by structure aware seq2seq learning"
    - "neural text generation from structured data with application to the biography domain"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"

Connected through hyperedge HE-3 (induced by source paper "tree structured semantic encoder with knowledge sharing for domain adaptation in natural language generation"):
  with members:
    - "variational cross domain natural language generation for spoken dialogue systems"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "the e2e dataset new challenges for end to end generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "improved semantic representations from tree structured long short term memory networks"
    - "multi domain dialog state tracking using recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "meta learning for low resource natural language generation in task oriented dialogue systems"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "meta learning for low resource neural machine translation"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "entity aware image caption generation"):
  with members:
    - "rich image captioning in the wild"
    - "captioning images with diverse objects"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "very deep convolutional networks for large scale image recognition"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-6 (induced by source paper "adversarial domain adaptation for variational neural language generation in dialogue systems"):
  with members:
    - "latent intention dialogue models"
    - "adversarial discriminative domain adaptation"
    - "semantic refinement gru based neural language generation for spoken dialogue systems"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "generating sentences from a continuous space"
    - "neural variational inference for text processing"

Connected through hyperedge HE-7 (induced by source paper "dual latent variable model for low resource natural language generation in dialogue systems"):
  with members:
    - "a hybrid convolutional variational autoencoder for text generation"
    - "deconvolutional latent variable model for text sequence matching"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "deconvolutional paragraph representation learning"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "hierarchical multiscale recurrent neural networks"
    - "generating sentences from a continuous space"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-8 (induced by source paper "semantic refinement gru based neural language generation for spoken dialogue systems"):
  with members:
    - "a network based end to end trainable task oriented dialogue system"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"

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)