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: "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.

Connected through hyperedge HE-1 (induced by source paper "metaphoric paraphrase generation"):
  with members:
    - "attention is all you need"
    - "generating conceptual metaphors from proposition stores"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "sentence level content planning and style specification for neural text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "argument generation with retrieval planning and realization"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural argument generation augmented with externally retrieved evidence"
    - "hierarchical neural story generation"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-3 (induced by source paper "constrained decoding for neural nlg from compositional representations in task oriented dialogue"):
  with members:
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "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"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "findings of the e2e nlg challenge"
    - "data to text generation with content selection and planning"

Connected through hyperedge HE-4 (induced by source paper "neural generation for czech data and baselines"):
  with members:
    - "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"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "topic aware neural response generation"

Connected through hyperedge HE-5 (induced by source paper "automatic quality estimation for natural language generation ranting jointly rating and ranking"):
  with members:
    - "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"
    - "referenceless quality estimation for natural language generation"
    - "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-6 (induced by source paper "maximizing stylistic control and semantic accuracy in nlg personality variation and discourse contrast"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "neural multivoice models for expressing novel personalities in dialog"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "learning neural templates for text generation"
    - "toward controlled generation of text"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "latent space factorisation and manipulation via matrix subspace projection"):
  with members:
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "deep nets what have they ever done for vision"
    - "learning latent subspaces in variational autoencoders"
    - "i revnet deep invertible networks"
    - "dirichlet variational autoencoder for text modeling"
    - "toward controlled generation of text"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-8 (induced by source paper "designing a symbolic intermediate representation for neural surface realization"):
  with members:
    - "universal dependency parsing from scratch"
    - "strategies for structuring story generation"
    - "neural text generation from rich semantic representations"
    - "generating high quality surface realizations using data augmentation and factored sequence models"
    - "human vs automatic metrics on the importance of correlation design"
    - "decoupling strategy and generation in negotiation dialogues"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

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)