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: "marian cost effective high quality neural machine translation in c"
  Abstract: This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task. We investigate combinations of teacher-student training, low-precision matrix products, auto-tuning and other methods to optimize the Transformer model on GPU and CPU. By further integrating these methods with the new averaging attention networks, a recently introduced faster Transformer variant, we create a number of high-quality, high-performance models on the GPU and CPU, dominating the Pareto frontier for this shared task.

Connected through hyperedge HE-1 (induced by source paper "learning accurate integer transformer machine translation models"):
  with members:
    - "asymmetric valleys beyond sharp and flat local minima"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "training tips for the transformer model"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-2 (induced by source paper "improving deep transformer with depth scaled initialization and merged attention"):
  with members:
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "generating long sequences with sparse transformers"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "blockwise parallel decoding for deep autoregressive models"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "adaptively sparse transformers"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "bert rediscovers the classical nlp pipeline"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "attention is not explanation"
    - "generating long sequences with sparse transformers"
    - "selective attention for context aware neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation with 4 bit precision and beyond"):
  with members:
    - "making asynchronous stochastic gradient descent work for transformers"
    - "a call for clarity in reporting bleu scores"
    - "pieces of eight 8 bit neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "findings of the second workshop on neural machine translation and generation"):
  with members:
    - "modeling latent sentence structure in neural machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "inducing grammars with and for neural machine translation"
    - "towards one shot learning for rare word translation with external experts"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "multi source neural machine translation with missing data"
    - "extreme adaptation for personalized neural 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)