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: "char2char generation with reranking for the e2e nlg challenge"
  Abstract: This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing delexicalization (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2seq model, which requires no pre/post-processing (delexicalization, tokenization or even lowercasing), with surprisingly good results. For further improvement, we explore two re-ranking approaches for scoring candidates. We also introduce a synthetic dataset creation procedure, which opens up a new way of creating artificial datasets for Natural Language Generation.

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"
    - "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"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"

Connected through hyperedge HE-2 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "neural machine translation with gumbel greedy decoding"
    - "listening while speaking speech chain by deep learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "improving quality and efficiency in plan based neural data to text generation"):
  with members:
    - "revisiting adversarial autoencoder for unsupervised word translation with cycle consistency and improved training"
    - "step by step separating planning from realization in neural data to text generation"
    - "neuralreg an end to end approach to referring expression generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "individual and domain adaptation in sentence planning for dialogue"

Connected through hyperedge HE-4 (induced by source paper "findings of the e2e nlg challenge"):
  with members:
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "crowd sourcing nlg data pictures elicit better data"
    - "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)