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: "the university of edinburgh s neural mt systems for wmt17"
  Abstract: This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks. We participated in 12 translation directions for news, translating between English and Czech, German, Latvian, Russian, Turkish and Chinese. For the biomedical task we submitted systems for English to Czech, German, Polish and Romanian. Our systems are neural machine translation systems trained with Nematus, an attentional encoder-decoder. We follow our setup from last year and build BPE-based models with parallel and back-translated monolingual training data. Novelties this year include the use of deep architectures, layer normalization, and more compact models due to weight tying and improvements in BPE segmentations. We perform extensive ablative experiments, reporting on the effectivenes of layer normalization, deep architectures, and different ensembling techniques.

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 "revisiting low resource neural machine translation a case study"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "revisiting character based neural machine translation with capacity and compression"
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "universal neural machine translation for extremely low resource languages"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"

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 "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"
    - "attention is all you need"
    - "slim embedding layers for recurrent neural language models"
    - "memory augmented neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "synchronous bidirectional inference for neural sequence generation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bridging the gap between training and inference for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "self attention with relative position representations"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "multilingual neural machine translation for zero resource languages"):
  with members:
    - "self attention with relative position representations"
    - "deep neural machine translation with weakly recurrent units"
    - "improving zero shot translation of low resource languages"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "nematus a toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "synchronous bidirectional neural machine translation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "learning to decode for future success"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "tagged back translation"):
  with members:
    - "text repair model for neural machine translation"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "a call for clarity in reporting bleu scores"
    - "explaining and generalizing back translation through wake sleep"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"

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)