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: "on using monolingual corpora in neural machine translation"
  Abstract: Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation. Arguably, one of the major factors behind this success has been the availability of high quality parallel corpora. In this work, we investigate how to leverage abundant monolingual corpora for neural machine translation. Compared to a phrase-based and hierarchical baseline, we obtain up to $1.96$ BLEU improvement on the low-resource language pair Turkish-English, and $1.59$ BLEU on the focused domain task of Chinese-English chat messages. While our method was initially targeted toward such tasks with less parallel data, we show that it also extends to high resource languages such as Cs-En and De-En where we obtain an improvement of $0.39$ and $0.47$ BLEU scores over the neural machine translation baselines, respectively.

Connected through hyperedge HE-1 (induced by source paper "cued wmt19 ewc lms"):
  with members:
    - "selective attention for context aware neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "improving the transformer translation model with document level context"
    - "simple fusion return of the language model"

Connected through hyperedge HE-2 (induced by source paper "low resource response generation with template prior"):
  with members:
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "text generation with exemplar based adaptive decoding"
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "universal neural machine translation for extremely low resource languages"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "learning neural templates for text generation"
    - "conversational ai the science behind the alexa prize"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-3 (induced by source paper "espresso a fast end to end neural speech recognition toolkit"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "parallelizable stack long short term memory"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "when does label smoothing help"
    - "who needs words lexicon free speech recognition"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"

Connected through hyperedge HE-4 (induced by source paper "can unconditional language models recover arbitrary sentences"):
  with members:
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "measuring the intrinsic dimension of objective landscapes"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "pseudolikelihood reranking with masked language models"):
  with members:
    - "passage re ranking with bert"
    - "effective sentence scoring method using bidirectional language model for speech recognition"
    - "pre trained language model representations for language generation"
    - "bertscore evaluating text generation with bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "language models with transformers"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "language modeling with deep transformers"

Connected through hyperedge HE-6 (induced by source paper "on extractive and abstractive neural document summarization with transformer language models"):
  with members:
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "controlling decoding for more abstractive summaries with copy based networks"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

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 "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"):
  with members:
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "selective encoding for abstractive sentence summarization"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "differentiable scheduled sampling for credit assignment"
    - "memory augmented neural networks with wormhole connections"
    - "controllable abstractive summarization"

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)