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: "multi source neural machine translation with missing data"
  Abstract: Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for multi-source neural machine translation (NMT) using an incomplete multilingual corpus in which some translations are missing. In practice, many multilingual corpora are not complete due to the difficulty to provide translations in all of the relevant languages (for example, in TED talks, most English talks only have subtitles for a small portion of the languages that TED supports). Existing studies on multi-source translation did not explicitly handle such situations. This study focuses on the use of incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts and examines a very simple implementation where missing source translations are replaced by a special symbol  . These methods allow us to use incomplete corpora both at training time and test time. In experiments with real incomplete multilingual corpora of TED Talks, the multi-source NMT with the   tokens achieved higher translation accuracies measured by BLEU than those by any one-to-one NMT systems.

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 "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

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"
    - "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-4 (induced by source paper "generating a common question from multiple documents using multi source encoder decoder models"):
  with members:
    - "passage re ranking with bert"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "deep communicating agents for abstractive summarization"
    - "input combination strategies for multi source transformer decoder"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-5 (induced by source paper "findings of the second workshop on neural machine translation and generation"):
  with members:
    - "modeling latent sentence structure in neural machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "inducing grammars with and for neural machine translation"
    - "towards one shot learning for rare word translation with external experts"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "marian cost effective high quality neural machine translation in c"
    - "extreme adaptation for personalized neural machine translation"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-6 (induced by source paper "input combination strategies for multi source transformer decoder"):
  with members:
    - "context aware neural machine translation learns anaphora resolution"
    - "imagination improves multimodal translation"
    - "attention is all you need"
    - "one model to learn them all"
    - "enabling multi source neural machine translation by concatenating source sentences in multiple languages"
    - "using the output embedding to improve language models"
    - "multi source neural translation"
    - "google s neural machine translation system bridging the gap between human and 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)