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: "pieces of eight 8 bit neural machine translation"
  Abstract: Neural machine translation has achieved levels of fluency and adequacy that would have been surprising a short time ago. Output quality is extremely relevant for industry purposes, however it is equally important to produce results in the shortest time possible, mainly for latency-sensitive applications and to control cloud hosting costs. In this paper we show the effectiveness of translating with 8-bit quantization for models that have been trained using 32-bit floating point values. Results show that 8-bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy.

Connected through hyperedge HE-1 (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"
    - "marian cost effective high quality neural machine translation in c"
    - "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-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"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "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 "sharing attention weights for fast transformer"):
  with members:
    - "recurrent stacking of layers for compact neural machine translation models"
    - "accelerating neural transformer via an average attention network"
    - "attention is all you need"
    - "mixed precision training"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "sequence level knowledge distillation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "scaling neural machine translation"):
  with members:
    - "weighted transformer network for machine translation"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "self attention with relative position representations"
    - "high accuracy low precision training"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "don t decay the learning rate increase the batch size"

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)