human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg"):
  Abstract: Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years. While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive. This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output. We present YelpNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains. The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics.

Includes members:
  Member "constrained decoding for neural nlg from compositional representations in task oriented dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "step by step separating planning from realization in neural data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "paraphrase generation with latent bag of words")
  Member "opennmt neural machine translation toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluating sequence to sequence learning models for if then program synthesis")
  Member "controlling personality based stylistic variation with neural natural language generators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "can neural generators for dialogue learn sentence planning and discourse structuring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational template machine for data to text generation")
  Member "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")

Question: Please tell me which class the source paper 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)