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 "dynamic oracle for neural machine translation in decoding phase"):
  Abstract: The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT). However, there exists discrepancy between training and inference in NMT when decoding, which may lead to serious problems since the model might be in a part of the state space it has never seen during training. To address the issue, Scheduled Sampling has been proposed. However, there are certain limitations in Scheduled Sampling and we propose two dynamic oracle-based methods to improve it. We manage to mitigate the discrepancy by changing the training process towards a less guided scheme and meanwhile aggregating the oracle's demonstrations. Experimental results show that the proposed approaches improve translation quality over standard NMT system.

Includes members:
  Member "scheduled sampling for sequence prediction with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "show and tell a neural image caption generator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "a reduction of imitation learning and structured prediction to no regret online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "search based structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "loss annealed gail for sample efficient and stable imitation learning")

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)