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: "multi representation ensembles and delayed sgd updates improve syntax based nmt"
  Abstract: We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We formulate beam search over such ensembles using WFSTs, and describe a delayed SGD update training procedure that is especially effective for long representations like linearized syntax. Our approach gives state-of-the-art performance on a difficult Japanese-English task.

Connected through hyperedge HE-1 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "widening the representation bottleneck in neural machine translation with lexical shortcuts"):
  with members:
    - "universal transformers"
    - "exploiting deep representations for neural machine translation"
    - "tensor2tensor for neural machine translation"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a call for clarity in reporting bleu scores"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "leveraging out of task data for end to end automatic speech translation"):
  with members:
    - "transformers with convolutional context for asr"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "espnet end to end speech processing toolkit"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"

Connected through hyperedge HE-4 (induced by source paper "ucam biomedical translation at wmt19 transfer learning multi domain ensembles"):
  with members:
    - "domain adaptive inference for neural machine translation"
    - "cued wmt19 ewc lms"
    - "a call for clarity in reporting bleu scores"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"

Connected through hyperedge HE-5 (induced by source paper "accelerating asynchronous stochastic gradient descent for neural machine translation"):
  with members:
    - "marian fast neural machine translation in c"
    - "don t use large mini batches use local sgd"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "sparse communication for distributed gradient descent"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "edinburgh neural machine translation systems for wmt 16"

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)