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: "neutron an implementation of the transformer translation model and its variants"
  Abstract: The Transformer translation model is easier to parallelize and provides better performance comparing with recurrent seq2seq models, which makes it popular among industry and research community. We implement Neutron in this work, including the Transformer model and several variants from most recent researches. It is easier to modify and provides comparable performance with interesting features while keep readability.

Connected through hyperedge HE-1 (induced by source paper "uds submission for the wmt 19 automatic post editing task"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "towards robust neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "improving the transformer translation model with document level context"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"):
  with members:
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "depth growing for neural machine translation"
    - "training deeper neural machine translation models with transparent attention"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "benefits of depth in neural networks"

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)