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: "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
  Abstract: Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed competitive performance against pure bilingual systems. Notably, in low-resource settings, it proved to work effectively and efficiently, thanks to shared representation space that is forced across languages and induces a sort of transfer-learning. Furthermore, multilingual NMT enables so-called zero-shot inference across language pairs never seen at training time. Despite the increasing interest in this framework, an in-depth analysis of what a multilingual NMT model is capable of and what it is not is still missing. Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation. Our analysis leverages multiple professional post-edits of automatic translations by several different systems and focuses both on automatic standard metrics (BLEU and TER) and on widely used error categories, which are lexical, morphology, and word order errors.

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 "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic neural machine translation using amr"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"

Connected through hyperedge HE-4 (induced by source paper "one to many multilingual end to end speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "end to end speech translation with knowledge distillation"
    - "achieving human parity on automatic chinese to english news translation"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "deep neural machine translation with weakly recurrent units"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-5 (induced by source paper "massively multilingual neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "achieving human parity on automatic chinese to english news translation"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "emerging language spaces learned from massively multilingual corpora"

Connected through hyperedge HE-6 (induced by source paper "context in neural machine translation a review of models and evaluations"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "adapting multilingual neural machine translation to unseen languages"):
  with members:
    - "generalized data augmentation for low resource translation"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "universal neural machine translation for extremely low resource languages"
    - "scaling neural machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "a comparative study on transformer vs rnn in speech applications"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "neural speech synthesis with transformer network"

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)