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: "using monolingual data in neural machine translation a systematic study"
  Abstract: Neural Machine Translation (MT) has radically changed the way systems are developed. A major difference with the previous generation (Phrase-Based MT) is the way monolingual target data, which often abounds, is used in these two paradigms. While Phrase-Based MT can seamlessly integrate very large language models trained on billions of sentences, the best option for Neural MT developers seems to be the generation of artificial parallel data through \textsl{back-translation} - a technique that fails to fully take advantage of existing datasets. In this paper, we conduct a systematic study of back-translation, comparing alternative uses of monolingual data, as well as multiple data generation procedures. Our findings confirm that back-translation is very effective and give new explanations as to why this is the case. We also introduce new data simulation techniques that are almost as effective, yet much cheaper to implement.

Connected through hyperedge HE-1 (induced by source paper "variational template machine for data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "the curious case of neural text degeneration"
    - "table to text generation with effective hierarchical encoder on three dimensions row column and time"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "describing a knowledge base"

Connected through hyperedge HE-2 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation"):
  with members:
    - "multilingual denoising pre training for neural machine translation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "scaling neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-3 (induced by source paper "tag less back translation"):
  with members:
    - "tagged back translation"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "iterative batch back translation for neural machine translation a conceptual model"):
  with members:
    - "tagged back translation"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "scaling neural machine translation"
    - "joint training for neural machine translation models with monolingual data"

Connected through hyperedge HE-5 (induced by source paper "domain translationese and noise in synthetic data for neural machine translation"):
  with members:
    - "tagged back translation"
    - "the effect of translationese in machine translation test sets"
    - "the source target domain mismatch problem in machine translation"
    - "ape at scale and its implications on mt evaluation biases"
    - "on the evaluation of machine translation systems trained with back translation"
    - "translationese in machine translation evaluation"
    - "marian fast neural machine translation in c"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-6 (induced by source paper "generalizing back translation in neural machine translation"):
  with members:
    - "analyzing uncertainty in neural machine translation"
    - "explaining and generalizing back translation through wake sleep"
    - "understanding back translation at scale"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "regularizing neural networks by penalizing confident output distributions"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "the source target domain mismatch problem in machine translation"):
  with members:
    - "the effect of translationese in machine translation test sets"
    - "on the evaluation of machine translation systems trained with back translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"
    - "rapid adaptation of neural machine translation to new languages"
    - "attention is all you need"
    - "bag of tricks for efficient text classification"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-8 (induced by source paper "bilingual is at least monolingual balm a novel translation algorithm that encodes monolingual priors"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "cold fusion training seq2seq models together with language models"
    - "multi30k multilingual english german image descriptions"
    - "improving neural machine translation models with monolingual data"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "neural machine translation by jointly learning to align and translate"

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)