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 "modeling fluency and faithfulness for diverse neural machine translation"):
  Abstract: Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution. However, the strategy casts all the portion of the distribution to the ground truth word and ignores other words in the target vocabulary even when the ground truth word cannot dominate the distribution. To address the problem of teacher forcing, we propose a method to introduce an evaluation module to guide the distribution of the prediction. The evaluation module accesses each prediction from the perspectives of fluency and faithfulness to encourage the model to generate the word which has a fluent connection with its past and future translation and meanwhile tends to form a translation equivalent in meaning to the source. The experiments on multiple translation tasks show that our method can achieve significant improvements over strong baselines.

Includes members:
  Member "bridging the gap between training and inference for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "synchronous bidirectional inference for neural sequence generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pseudo bidirectional decoding for local sequence transduction")
  Member "beyond bleu training neural machine translation with semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gret global representation enhanced transformer")
  Member "asynchronous bidirectional decoding for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pseudo bidirectional decoding for local sequence transduction")
  Member "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fine tuning language models from human preferences")
  Member "a study of reinforcement learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "greedy search with probabilistic n gram matching for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimizing the bag of ngrams difference for non autoregressive neural machine 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)