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: "simultaneous translation with flexible policy via restricted imitation learning"
  Abstract: Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged model using reinforcement learning. We propose a much simpler single model that adds a `delay' token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on Chinese English simultaneous translation show that our work leads to flexible policies that achieve better BLEU scores and lower latencies compared to both fixed and RL-learned policies.

Connected through hyperedge HE-1 (induced by source paper "learning coupled policies for simultaneous machine translation"):
  with members:
    - "thinking slow about latency evaluation for simultaneous machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "recent advances in imitation learning from observation"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "low latency neural speech translation"

Connected through hyperedge HE-2 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"

Connected through hyperedge HE-3 (induced by source paper "speculative beam search for simultaneous translation"):
  with members:
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "learning to stop in structured prediction for neural machine translation"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "correcting length bias in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-4 (induced by source paper "monotonic multihead attention"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "online and linear time attention by enforcing monotonic alignments"

Connected through hyperedge HE-5 (induced by source paper "simpler and faster learning of adaptive policies for simultaneous translation"):
  with members:
    - "speculative beam search for simultaneous translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "can neural machine translation do simultaneous translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"):
  with members:
    - "you may not need attention"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "learning to translate in real time with neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "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)