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: "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
  Abstract: Attentional sequence-to-sequence models have become the new standard for machine translation, but one challenge of such models is a significant increase in training and decoding cost compared to phrase-based systems. Here, we focus on efficient decoding, with a goal of achieving accuracy close the state-of-the-art in neural machine translation (NMT), while achieving CPU decoding speed/throughput close to that of a phrasal decoder. #R##N#We approach this problem from two angles: First, we describe several techniques for speeding up an NMT beam search decoder, which obtain a 4.4x speedup over a very efficient baseline decoder without changing the decoder output. Second, we propose a simple but powerful network architecture which uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked fully-connected layers applied at every timestep. This architecture achieves similar accuracy to a deep recurrent model, at a small fraction of the training and decoding cost. By combining these techniques, our best system achieves a very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014, while decoding at 100 words/sec on single-threaded CPU. We believe this is the best published accuracy/speed trade-off of an NMT system.

Connected through hyperedge HE-1 (induced by source paper "sequence generation from both sides to the middle"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "accelerating neural transformer via an average attention network"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "non autoregressive neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "in training matrix factorization for parameter frugal neural machine translation"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training tips for the transformer model"
    - "pieces of eight 8 bit neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "unfolding and shrinking neural machine translation ensembles"
    - "factorization tricks for lstm networks"
    - "mixed precision training"

Connected through hyperedge HE-3 (induced by source paper "accelerating transformer decoding via a hybrid of self attention and recurrent neural network"):
  with members:
    - "accelerating neural transformer via an average attention network"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "addressing the rare word problem in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "universal neural machine translation for extremely low resource languages"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "convolutional sequence to sequence learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "training deeper neural machine translation models with transparent attention"):
  with members:
    - "deep contextualized word representations"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "deep architectures for neural machine translation"
    - "skip connections as effective symmetry breaking"

Connected through hyperedge HE-6 (induced by source paper "the best of both worlds combining recent advances in neural machine translation"):
  with members:
    - "stronger baselines for trustable results in neural machine translation"
    - "checkpoint ensembles ensemble methods from a single training process"
    - "revisiting distributed synchronous sgd"
    - "a decomposable attention model for natural language inference"
    - "language modeling with gated convolutional networks"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-7 (induced by source paper "accelerating nmt batched beam decoding with lmbr posteriors for deployment"):
  with members:
    - "neural machine translation decoding with terminology constraints"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "six challenges for neural machine translation"
    - "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"
    - "interactive attention for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-8 (induced by source paper "pieces of eight 8 bit neural machine translation"):
  with members:
    - "ensemble distillation for neural machine translation"
    - "six challenges for neural machine translation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "kblas an optimized library for dense matrix vector multiplication on gpu accelerators"

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)