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: "effective strategies in zero shot neural machine translation"
  Abstract: In this paper, we proposed two strategies which can be applied to a multilingual neural machine translation system in order to better tackle zero-shot scenarios despite not having any parallel corpus. The experiments show that they are effective in terms of both performance and computing resources, especially in multilingual translation of unbalanced data in real zero-resourced condition when they alleviate the language bias problem.

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 "a 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"
    - "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"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-3 (induced by source paper "consistency by agreement in zero shot neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "tensor2tensor for neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "style transfer through back translation"
    - "large scale distributed neural network training through online distillation"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-4 (induced by source paper "multilingual end to end speech translation"):
  with members:
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "tied multitask learning for neural speech translation"
    - "transfer learning of language independent end to end asr with language model fusion"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"

Connected through hyperedge HE-5 (induced by source paper "tagged back translation"):
  with members:
    - "text repair model for neural machine translation"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "a call for clarity in reporting bleu scores"
    - "explaining and generalizing back translation through wake sleep"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-6 (induced by source paper "pivot based transfer learning for neural machine translation between non english languages"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "massively multilingual neural machine translation"
    - "filtering and mining parallel data in a joint multilingual space"
    - "trivial transfer learning for low resource neural machine translation"
    - "triangular architecture for rare language translation"
    - "improving zero shot translation of low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "zero shot dual machine translation"

Connected through hyperedge HE-7 (induced by source paper "improving zero shot translation with language independent constraints"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "controlling neural machine translation formality with synthetic supervision"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "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"

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)