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: "syntactically supervised transformers for faster neural machine translation"
  Abstract: Standard decoders for neural machine translation autoregressively generate a single target token per time step, which slows inference especially for long outputs. While architectural advances such as the Transformer fully parallelize the decoder computations at training time, inference still proceeds sequentially. Recent developments in non- and semi- autoregressive decoding produce multiple tokens per time step independently of the others, which improves inference speed but deteriorates translation quality. In this work, we propose the syntactically supervised Transformer (SynST), which first autoregressively predicts a chunked parse tree before generating all of the target tokens in one shot conditioned on the predicted parse. A series of controlled experiments demonstrates that SynST decodes sentences ~ 5x faster than the baseline autoregressive Transformer while achieving higher BLEU scores than most competing methods on En-De and En-Fr datasets.

Connected through hyperedge HE-1 (induced by source paper "minimizing the bag of ngrams difference for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "imitation learning for non autoregressive neural machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "pnat non autoregressive transformer by position learning"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "guiding non autoregressive neural machine translation decoding with reordering information"
    - "a study of reinforcement learning for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "understanding knowledge distillation in non autoregressive machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "retrieving sequential information for non autoregressive neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "imitation learning for non autoregressive neural machine translation"
    - "mixture models for diverse machine translation tricks of the trade"

Connected through hyperedge HE-3 (induced by source paper "guiding non autoregressive neural machine translation decoding with reordering information"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "retrieving sequential information for non autoregressive neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "recursive neural network based preordering for english to japanese machine translation"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)