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: "the neural noisy channel"
  Abstract: We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use.

Connected through hyperedge HE-1 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training"):
  with members:
    - "putting machine translation in context with the noisy channel model"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "context aware monolingual repair for neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "revisiting self training for neural sequence generation"
    - "fill in the blanks imputing missing sentences for larger context neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "modeling coherence for discourse neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "putting machine translation in context with the noisy channel model"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "pre trained language model representations for language generation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "repurposing decoder transformer language models for abstractive summarization"
    - "pretraining based natural language generation for text summarization"
    - "defending against neural fake news"

Connected through hyperedge HE-3 (induced by source paper "relevance promoting language model for short text conversation"):
  with members:
    - "generating multiple diverse responses with multi mapping and posterior mapping selection"
    - "dlgnet a transformer based model for dialogue response generation"
    - "the curious case of neural text degeneration"
    - "comparison of diverse decoding methods from conditional language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "a discrete cvae for response generation on short text conversation"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-4 (induced by source paper "plug and play language models a simple approach to controlled text generation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "the curious case of neural text degeneration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuning language models from human preferences"
    - "putting machine translation in context with the noisy channel model"

Connected through hyperedge HE-5 (induced by source paper "how decoding strategies affect the verifiability of generated text"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "mixture content selection for diverse sequence generation"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "the curious case of neural text degeneration"
    - "tmlab generative enhanced model gem for adversarial attacks"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-6 (induced by source paper "the curious case of neural text degeneration"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "on accurate evaluation of gans for language generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"

Connected through hyperedge HE-7 (induced by source paper "encoder agnostic adaptation for conditional language generation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "language models with transformers"
    - "cross lingual language model pretraining"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "unified language model pre training for natural language understanding and generation"

Connected through hyperedge HE-8 (induced by source paper "domain robustness in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "a call for clarity in reporting bleu scores"
    - "bi directional differentiable input reconstruction for low resource neural machine translation"
    - "attention is all you need"
    - "extending defensive distillation"
    - "synthetic and natural noise both break neural machine translation"

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)