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 multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
  Abstract: We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.

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 "post editese an exacerbated translationese"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-3 (induced by source paper "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"):
  with members:
    - "self attention with relative position representations"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "neural versus phrase based machine translation quality a case study"

Connected through hyperedge HE-4 (induced by source paper "analyzing uncertainty in neural machine translation"):
  with members:
    - "a challenge set approach to evaluating machine translation"
    - "on calibration of modern neural networks"
    - "analyzing neural mt search and model performance"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "variational neural machine translation"
    - "diverse beam search decoding diverse solutions from neural sequence models"

Connected through hyperedge HE-5 (induced by source paper "what level of quality can neural machine translation attain on literary text"):
  with members:
    - "fine grained human evaluation of neural versus phrase based machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural versus phrase based machine translation quality a case study"
    - "edinburgh neural machine translation systems for wmt 16"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "quantitative fine grained human evaluation of machine translation systems a case study on english to croatian"):
  with members:
    - "fine grained human evaluation of neural versus phrase based machine translation"
    - "neural versus phrase based machine translation quality a case study"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "fluency over adequacy a pilot study in measuring user trust in imperfect mt"):
  with members:
    - "six challenges for neural machine translation"
    - "neural versus phrase based machine translation quality a case study"

Connected through hyperedge HE-8 (induced by source paper "what do neural machine translation models learn about morphology"):
  with members:
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "character based neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "large scale machine translation between arabic and hebrew available corpora and initial results"

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)