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: "toward multilingual neural machine translation with universal encoder and decoder"
  Abstract: In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.

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 "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "morphological word embeddings"
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-3 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers"):
  with members:
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "achieving human parity on automatic chinese to english news translation"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "deep recurrent models with fast forward connections for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "from speech to speech translation to automatic dubbing"):
  with members:
    - "controlling the output length of neural machine translation"
    - "in other news a bi style text to speech model for synthesizing newscaster voice with limited data"
    - "prosodic phrase alignment for machine dubbing"
    - "effect of data reduction on sequence to sequence neural tts"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "double path networks for sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "a study of multilingual neural machine translation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multilingual neural machine translation with language clustering"
    - "language graph distillation for low resource machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"

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)