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: "the missing ingredient in zero shot neural machine translation"
  Abstract: Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages. Despite various approaches to train such models, they have difficulty with zero-shot translation: translating between language pairs that were not together seen during training. In this paper we first diagnose why state-of-the-art multilingual NMT models that rely purely on parameter sharing, fail to generalize to unseen language pairs. We then propose auxiliary losses on the NMT encoder that impose representational invariance across languages. Our simple approach vastly improves zero-shot translation quality without regressing on supervised directions. For the first time, on WMT14 English-FrenchGerman, we achieve zero-shot performance that is on par with pivoting. We also demonstrate the easy scalability of our approach to multiple languages on the IWSLT 2017 shared task.

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"
    - "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-2 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"

Connected through hyperedge HE-3 (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"
    - "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"
    - "parameter sharing methods for multilingual self attentional translation models"

Connected through hyperedge HE-4 (induced by source paper "consistency by agreement in zero shot neural machine translation"):
  with members:
    - "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"
    - "zero shot dual machine translation"

Connected through hyperedge HE-5 (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"
    - "consistency by agreement in zero shot neural machine translation"
    - "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"
    - "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-6 (induced by source paper "towards interlingua neural machine translation"):
  with members:
    - "unsupervised statistical machine translation"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "fast decoding in sequence models using discrete latent variables"
    - "a neural interlingua for multilingual machine translation"
    - "phrase based neural unsupervised machine translation"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-7 (induced by source paper "improved zero shot neural machine translation via ignoring spurious correlations"):
  with members:
    - "cross lingual language model pretraining"
    - "universal neural machine translation for extremely low resource languages"
    - "zero resource neural machine translation with multi agent communication game"
    - "meta learning for low resource neural machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "zero shot dual machine translation"
    - "a teacher student framework for zero resource neural machine translation"

Connected through hyperedge HE-8 (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"
    - "consistency by agreement in zero shot neural machine translation"
    - "cross lingual language model pretraining"
    - "massively multilingual 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"

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)