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: "referenceless quality estimation for natural language generation"
  Abstract: Traditional automatic evaluation measures for natural language generation (NLG) use costly human-authored references to estimate the quality of a system output. In this paper, we propose a referenceless quality estimation (QE) approach based on recurrent neural networks, which predicts a quality score for a NLG system output by comparing it to the source meaning representation only. Our method outperforms traditional metrics and a constant baseline in most respects; we also show that synthetic data helps to increase correlation results by 21% compared to the base system. Our results are comparable to results obtained in similar QE tasks despite the more challenging setting.

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 "communication based evaluation for natural language generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "pragmatically informative text generation"
    - "generating bilingual pragmatic color references"
    - "translating neuralese"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "learning to summarize radiology findings"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "sentence level fluency evaluation references help but can be spared"

Connected through hyperedge HE-3 (induced by source paper "automatic quality estimation for natural language generation ranting jointly rating and ranking"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "reference less quality estimation of text simplification systems"
    - "end to end content and plan selection for data to text generation"
    - "a practical approach to dialogue response generation in closed domains"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "challenges in data to document generation"
    - "adversarial evaluation of dialogue models"

Connected through hyperedge HE-4 (induced by source paper "on conducting better validation studies of automatic metrics in natural language generation evaluation"):
  with members:
    - "deep contextualized word representations"
    - "a call for clarity in reporting bleu scores"
    - "evaluation of automatic video captioning using direct assessment"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "massive exploration of neural machine translation architectures"

Connected through hyperedge HE-5 (induced by source paper "latent topic conversational models"):
  with members:
    - "diversity promoting bayesian learning of latent variable models"
    - "latent intention dialogue models"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "latent variable dialogue models and their diversity"
    - "steering output style and topic in neural response generation"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"

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"
    - "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"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "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 "the price of debiasing automatic metrics in natural language evaluation"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "making neural qa as simple as possible but not simpler"
    - "get to the point summarization with pointer generator networks"
    - "the narrativeqa reading comprehension challenge"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "a deep reinforced model for abstractive summarization"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-8 (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"
    - "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"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "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"

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)