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: "rapid adaptation of neural machine translation to new languages"
  Abstract: This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual "seed models", which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of "similar-language regularization", where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings. Code to reproduce experiments at this https URL

Connected through hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-2 (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-3 (induced by source paper "effective cross lingual transfer of neural machine translation models without shared vocabularies"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-4 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "self supervised generalisation with meta auxiliary learning"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"

Connected through hyperedge HE-5 (induced by source paper "revisiting low resource neural machine translation a case study"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "revisiting character based neural machine translation with capacity and compression"
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "universal neural machine translation for extremely low resource languages"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-6 (induced by source paper "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "double path networks for sequence to sequence learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a study of multilingual neural machine translation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multilingual neural machine translation with language clustering"
    - "language graph distillation for low resource machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"

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)