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 "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  Abstract: This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Introducing novel automatic and human metrics, we compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates. Seq2seq-based systems have demonstrated a great potential for NLG in the challenge. We find that seq2seq systems generally score high in terms of word-overlap metrics and human evaluations of naturalness -- with the winning SLUG system (Juraska et al., 2018) being seq2seq-based. However, vanilla seq2seq models often fail to correctly express a given meaning representation if they lack a strong semantic control mechanism applied during decoding. Moreover, seq2seq models can be outperformed by hand-engineered systems in terms of overall quality, as well as complexity, length and diversity of outputs. This research has influenced, inspired and motivated a number of recent studies outwith the original competition, which we also summarise as part of this paper.

Includes members:
  Member "curate and generate a corpus and method for joint control of semantics and style in neural nlg":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "natural language generation at scale a case study for open domain question answering")
  Member "constrained decoding for neural nlg from compositional representations in task oriented dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg")
  Member "dual supervised learning for natural language understanding and generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning")
  Member "copy mechanism and tailored training for character based data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a hierarchical model for data to text generation")
  Member "char2char generation with reranking for the e2e nlg challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models")
  Member "rankme reliable human ratings for natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "controlling personality based stylistic variation with neural natural language generators":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation")
  Member "can neural generators for dialogue learn sentence planning and discourse structuring":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational template machine for data to text generation")

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)