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 in dialogue using lexicalized and delexicalized data"
  Abstract: Natural language generation plays a critical role in spoken dialogue systems. We present a new approach to natural language generation for task-oriented dialogue using recurrent neural networks in an encoder-decoder framework. In contrast to previous work, our model uses both lexicalized and delexicalized components i.e. slot-value pairs for dialogue acts, with slots and corresponding values aligned together. This allows our model to learn from all available data including the slot-value pairing, rather than being restricted to delexicalized slots. We show that this helps our model generate more natural sentences with better grammar. We further improve our model's performance by transferring weights learnt from a pretrained sentence auto-encoder. Human evaluation of our best-performing model indicates that it generates sentences which users find more appealing.

Connected through hyperedge HE-1 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-2 (induced by source paper "abstractive dialog summarization with semantic scaffolds"):
  with members:
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "bottom up abstractive summarization"
    - "dial2desc end to end dialogue description generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-3 (induced by source paper "neural generation for czech data and baselines"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "end to end content and plan selection for data to text generation"
    - "rdf2pt generating brazilian portuguese texts from rdf data"
    - "are all languages equally hard to language model"
    - "predicting target language ccg supertags improves neural machine translation"
    - "modeling target side inflection in neural machine translation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "topic aware neural response generation"

Connected through hyperedge HE-4 (induced by source paper "global to local memory pointer networks for task oriented dialogue"):
  with members:
    - "global locally self attentive dialogue state tracker"
    - "attention is all you need"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "learning to attend copy and generate for session based query suggestion"
    - "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"
    - "memory enhanced decoder for neural machine translation"
    - "a network based end to end trainable task oriented dialogue system"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-5 (induced by source paper "semantically conditioned dialog response generation via hierarchical disentangled self attention"):
  with members:
    - "xl nbt a cross lingual neural belief tracking framework"
    - "global locally self attentive dialogue state tracker"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"
    - "latent intention dialogue models"
    - "attention is all you need"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "multi domain neural network language generation for spoken dialogue systems"

Connected through hyperedge HE-6 (induced by source paper "char2char generation with reranking for the e2e nlg challenge"):
  with members:
    - "neural amr sequence to sequence models for parsing and generation"
    - "referenceless quality estimation for natural language generation"
    - "beam search strategies for neural machine translation"
    - "learning to generate one sentence biographies from wikidata"
    - "massive exploration of neural machine translation architectures"
    - "the e2e dataset new challenges for end to end generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "rankme reliable human ratings for natural language generation"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "referenceless quality estimation for natural language generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "challenges in data to document generation"
    - "the e2e dataset new challenges for end to end generation"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "natural language generation enhances human decision making with uncertain information"
    - "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking"

Connected through hyperedge HE-8 (induced by source paper "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"):
  with members:
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "adversarial evaluation of dialogue models"
    - "crowd sourcing nlg data pictures elicit better data"
    - "deltableu a discriminative metric for generation tasks with intrinsically diverse targets"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

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)