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: "improved zero shot neural machine translation via ignoring spurious correlations"
  Abstract: Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings. However, naive training for zero-shot NMT easily fails, and is sensitive to hyper-parameter setting. The performance typically lags far behind the more conventional pivot-based approach which translates twice using a third language as a pivot. In this work, we address the degeneracy problem due to capturing spurious correlations by quantitatively analyzing the mutual information between language IDs of the source and decoded sentences. Inspired by this analysis, we propose to use two simple but effective approaches: (1) decoder pre-training; (2) back-translation. These methods show significant improvement (4~22 BLEU points) over the vanilla zero-shot translation on three challenging multilingual datasets, and achieve similar or better results than the pivot-based approach.

Connected through hyperedge HE-1 (induced by source paper "multilingual denoising pre training for neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "pre trained language model representations for language generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretrained language models for document level neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "a multilingual view of unsupervised machine translation"):
  with members:
    - "a probabilistic formulation of unsupervised text style transfer"
    - "multilingual denoising pre training for neural machine translation"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "explicit cross lingual pre training for unsupervised machine translation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "consistency by agreement in zero shot neural machine translation"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-3 (induced by source paper "cross lingual pre training based transfer for zero shot neural machine translation"):
  with members:
    - "pivot based transfer learning for neural machine translation between non english languages"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "consistency by agreement in zero shot neural machine translation"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "cross lingual natural language generation via pre training"):
  with members:
    - "denoising based sequence to sequence pre training for text generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "ncls neural cross lingual summarization"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual training for automatic question generation"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-5 (induced by source paper "evaluating the supervised and zero shot performance of multi lingual translation models"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a neural interlingua for multilingual machine translation"

Connected through hyperedge HE-6 (induced by source paper "zero shot paraphrase generation with multilingual language models"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "decomposable neural paraphrase generation"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "the missing ingredient in zero shot neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "unsupervised natural language generation with denoising autoencoders"

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)