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: "character based neural machine translation"
  Abstract: Neural Machine Translation (MT) has reached state-of-the-art results. However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and morphologically rich languages. In this paper, we propose a neural MT system using character-based embeddings in combination with convolutional and highway layers to replace the standard lookup-based word representations. The resulting unlimited-vocabulary and affix-aware source word embeddings are tested in a state-of-the-art neural MT based on an attention-based bidirectional recurrent neural network. The proposed MT scheme provides improved results even when the source language is not morphologically rich. Improvements up to 3 BLEU points are obtained in the German-English WMT task.

Connected through hyperedge HE-1 (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"
    - "the missing ingredient in zero shot neural machine translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "lattice based transformer encoder for neural machine translation"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "chinese word segmentation another decade review 2007 2017"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "revisiting character based neural machine translation with capacity and compression"
    - "nict s neural and statistical machine translation systems for the wmt18 news translation task"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "finding better subword segmentation for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-4 (induced by source paper "shared private bilingual word embeddings for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "slim embedding layers for recurrent neural language models"

Connected through hyperedge HE-5 (induced by source paper "a universal parent model for low resource neural machine translation transfer"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "rapid adaptation of neural machine translation to new languages"
    - "transfer learning in multilingual neural machine translation with dynamic vocabulary"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "who needs words lexicon free speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "comparison of decoding strategies for ctc acoustic models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "how large a vocabulary does text classification need a variational approach to vocabulary selection"):
  with members:
    - "bayesian compression for natural language processing"
    - "compressing recurrent neural network with tensor train"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "learning structured sparsity in deep neural networks"
    - "enhanced lstm for natural language inference"
    - "fully character level neural machine translation without explicit segmentation"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-8 (induced by source paper "exploring the robustness of nmt systems to nonsensical inputs"):
  with members:
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "detecting egregious responses in neural sequence to sequence models"
    - "attention is all you need"

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)