human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning coupled policies for simultaneous machine translation"):
  Abstract: In simultaneous machine translation, the system needs to incrementally generate the output translation before the input sentence ends. This is a coupled decision process consisting of a programmer and interpreter. The programmer's policy decides about when to WRITE the next output or READ the next input, and the interpreter's policy decides what word to write. We present an imitation learning (IL) approach to efficiently learn effective coupled programmer-interpreter policies. To enable IL, we present an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. We attribute the effectiveness of the learned coupled policies to (i) scheduled sampling addressing the coupled exposure bias, and (ii) quality of oracle actions capturing enough information from the partial input before writing the output. Experiments show our method outperforms strong baselines in terms of translation quality and delay, when translating from German/Arabic/Czech/Bulgarian/Romanian to English.

Includes members:
  Member "simultaneous translation with flexible policy via restricted imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report")
  Member "thinking slow about latency evaluation for simultaneous machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotonic infinite lookback attention for simultaneous machine translation")
  Member "bridging the gap between training and inference for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "recent advances in imitation learning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adversarial and conditional state space model for imitation learning")
  Member "monotonic infinite lookback attention for simultaneous machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "controlling computation versus quality for neural sequence models")
  Member "simpler and faster learning of adaptive policies for simultaneous translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speculative beam search for simultaneous translation")
  Member "direct speech to speech translation with a sequence to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "speech to speech translation between untranscribed unknown languages")
  Member "incremental decoding and training methods for simultaneous translation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "speculative beam search for simultaneous translation")

Question: Please tell me which class the source paper 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)