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: "crowd sourcing nlg data pictures elicit better data"
  Abstract: Recent advances in corpus-based Natural Language Generation (NLG) hold the promise of being easily portable across domains, but require costly training data, consisting of meaning representations (MRs) paired with Natural Language (NL) utterances. In this work, we propose a novel framework for crowdsourcing high quality NLG training data, using automatic quality control measures and evaluating different MRs with which to elicit data. We show that pictorial MRs result in better NL data being collected than logic-based MRs: utterances elicited by pictorial MRs are judged as significantly more natural, more informative, and better phrased, with a significant increase in average quality ratings (around 0.5 points on a 6-point scale), compared to using the logical MRs. As the MR becomes more complex, the benefits of pictorial stimuli increase. The collected data will be released as part of this submission.

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 "semantic noise matters for neural natural language generation"):
  with members:
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "object hallucination in image captioning"
    - "findings of the e2e nlg challenge"
    - "challenges in data to document generation"
    - "six challenges for neural machine translation"
    - "multi domain neural network language generation for spoken dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "controlling personality based stylistic variation with neural natural language generators"):
  with members:
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"
    - "harvesting creative templates for generating stylistically varied restaurant reviews"
    - "style transfer in text exploration and evaluation"
    - "controllable abstractive summarization"
    - "a persona based neural conversation model"
    - "contextual lstm clstm models for large scale nlp tasks"

Connected through hyperedge HE-4 (induced by source paper "neural multivoice models for expressing novel personalities in dialog"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"
    - "controllable abstractive summarization"
    - "the e2e dataset new challenges for end to end generation"
    - "a persona based neural conversation model"
    - "a context aware natural language generator for dialogue systems"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"

Connected through hyperedge HE-5 (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"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "adversarial evaluation of dialogue models"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"):
  with members:
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "controlling linguistic style aspects in neural language generation"
    - "harvesting creative templates for generating stylistically varied restaurant reviews"
    - "the e2e dataset new challenges for end to end generation"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "a deep ensemble model with slot alignment for sequence to sequence natural language generation"):
  with members:
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "findings of the e2e nlg challenge"):
  with members:
    - "char2char generation with reranking for the e2e nlg challenge"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

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)