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: "zero shot dual machine translation"
  Abstract: Neural Machine Translation (NMT) systems rely on large amounts of parallel data. This is a major challenge for low-resource languages. Building on recent work on unsupervised and semi-supervised methods, we present an approach that combines zero-shot and dual learning. The latter relies on reinforcement learning, to exploit the duality of the machine translation task, and requires only monolingual data for the target language pair. Experiments show that a zero-shot dual system, trained on English-French and English-Spanish, outperforms by large margins a standard NMT system in zero-shot translation performance on Spanish-French (both directions). The zero-shot dual method approaches the performance, within 2.2 BLEU points, of a comparable supervised setting. Our method can obtain improvements also on the setting where a small amount of parallel data for the zero-shot language pair is available. Adding Russian, to extend our experiments to jointly modeling 6 zero-shot translation directions, all directions improve between 4 and 15 BLEU points, again, reaching performance near that of the supervised setting.

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 "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-3 (induced by source paper "deep q learning with q matrix transfer learning for novel fire evacuation environment"):
  with members:
    - "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
    - "taskonomy disentangling task transfer learning"
    - "unity a general platform for intelligent agents"
    - "large scale distributed neural network training through online distillation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "target transfer q learning and its convergence analysis"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-4 (induced by source paper "improved zero shot neural machine translation via ignoring spurious correlations"):
  with members:
    - "cross lingual language model pretraining"
    - "the missing ingredient in zero shot neural machine translation"
    - "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"
    - "a teacher student framework for zero resource neural machine translation"

Connected through hyperedge HE-5 (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"
    - "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"

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"
    - "understanding back translation at scale"

Connected through hyperedge HE-7 (induced by source paper "zero shot paraphrase generation with multilingual language models"):
  with members:
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "the missing ingredient in zero shot neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "universal neural machine translation for extremely low resource languages"
    - "triangular architecture for rare language translation"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "the best of both worlds combining recent advances in 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.CL(Computation and Language)