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: "consistency by agreement in zero shot neural machine translation"
  Abstract: Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization---a challenging setup that tests models on translation directions they have not been optimized for at training time. To solve the problem, we (i) reformulate multilingual translation as probabilistic inference, (ii) define the notion of zero-shot consistency and show why standard training often results in models unsuitable for zero-shot tasks, and (iii) introduce a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. We test our multilingual NMT models on multiple public zero-shot translation benchmarks (IWSLT17, UN corpus, Europarl) and show that agreement-based learning often results in 2-3 BLEU zero-shot improvement over strong baselines without any loss in performance on supervised translation directions.

Connected through hyperedge HE-1 (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"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "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"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-2 (induced by source paper "investigating multilingual nmt representations at scale"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "multilingual neural machine translation with language clustering"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "zero shot cross lingual classification using multilingual neural machine translation"

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"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "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 "evaluating the supervised and zero shot performance of multi lingual translation models"):
  with members:
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "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-5 (induced by source paper "improving zero shot translation with language independent constraints"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "the missing ingredient in zero shot neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "controlling neural machine translation formality with synthetic supervision"):
  with members:
    - "multi task neural models for translating between styles within and across languages"
    - "the missing ingredient in zero shot neural machine translation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "identifying semantic divergences in parallel text without annotations"
    - "contextual parameter generation for universal neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "monolingual and cross lingual zero shot style transfer"
    - "sockeye a toolkit for neural machine translation"

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.LG(Machine Learning)