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: "regularization techniques for fine tuning in neural machine translation"
  Abstract: We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this scenario, overfitting is a major challenge. We investigate a number of techniques to reduce overfitting and improve transfer learning, including regularization techniques such as dropout and L2-regularization towards an out-of-domain prior. In addition, we introduce tuneout, a novel regularization technique inspired by dropout. We apply these techniques, alone and in combination, to neural machine translation, obtaining improvements on IWSLT datasets for English->German and English->Russian. We also investigate the amounts of in-domain training data needed for domain adaptation in NMT, and find a logarithmic relationship between the amount of training data and gain in BLEU score.

Connected through hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a constructive prediction of the generalization error across scales"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "on the convergence rate of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "simple scalable adaptation for neural machine translation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "non parametric adaptation for neural machine translation"
    - "massively multilingual neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-5 (induced by source paper "mixout effective regularization to finetune large scale pretrained language models"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network acceptability judgments"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

Connected through hyperedge HE-6 (induced by source paper "improving robustness of machine translation with synthetic noise"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "mtnt a testbed for machine translation of noisy text"
    - "synthetic and natural noise both break neural machine translation"
    - "six challenges for neural machine translation"
    - "domain control for neural machine translation"
    - "using the output embedding to improve language models"
    - "robsut wrod reocginiton via semi character recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "domain adaptive inference for neural machine translation"):
  with members:
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "six challenges for neural machine translation"
    - "neural machine translation training in a multi domain scenario"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-8 (induced by source paper "domain adaptation for part of speech tagging of noisy user generated text"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "ncrf an open source neural sequence labeling toolkit"
    - "the best of both worlds lexical resources to improve low resource part of speech tagging"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural architectures for named entity recognition"
    - "efficient estimation of word representations in vector space"

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)