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: We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.

Connected through hyperedge HE-1 (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"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-5 (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-6 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-7 (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-8 (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"

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)