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: "incorporating discriminator in sentence generation a gibbs sampling method"
  Abstract: Generating plausible and fluent sentence with desired properties has long been a challenge. Most of the recent works use recurrent neural networks (RNNs) and their variants to predict following words given previous sequence and target label. In this paper, we propose a novel framework to generate constrained sentences via Gibbs Sampling. The candidate sentences are revised and updated iteratively, with sampled new words replacing old ones. Our experiments show the effectiveness of the proposed method to generate plausible and diverse sentences.

Connected through hyperedge HE-1 (induced by source paper "araml a stable adversarial training framework for text generation"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "language gans falling short"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"

Connected through hyperedge HE-2 (induced by source paper "bilingual gan a step towards parallel text generation"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "phrase based neural unsupervised machine translation"
    - "generating continuous representations of medical texts"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "adversarially regularized autoencoders"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "fiction sentence expansion and enhancement via focused objective and novelty curve sampling"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "unsupervised sentence compression using denoising auto encoders"
    - "style transfer through back translation"
    - "hierarchical neural story generation"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "steering output style and topic in neural response generation"

Connected through hyperedge HE-4 (induced by source paper "cgmh constrained sentence generation by metropolis hastings sampling"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "style transfer from non parallel text by cross alignment"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"

Connected through hyperedge HE-5 (induced by source paper "salsa text self attentive latent space based adversarial text generation"):
  with members:
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "spectral normalization for generative adversarial networks"
    - "towards diverse text generation with inverse reinforcement learning"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "texygen a benchmarking platform for text generation models"
    - "character level language modeling with deeper self attention"
    - "toward controlled generation of text"
    - "attention is all you need"

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)