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 "cost sensitive training for autoregressive models"):
  Abstract: Training autoregressive models to better predict under the test metric, instead of maximizing the likelihood, has been reported to be beneficial in several use cases but brings additional complications, which prevent wider adoption. In this paper, we follow the learning-to-search approach (Daume III et al., 2009; Leblond et al., 2018) and investigate its several components. First, we propose a way to construct a reference policy based on an alignment between the model output and ground truth. Our reference policy is optimal when applied to the Kendall-tau distance between permutations (appear in the task of word ordering) and helps when working with the METEOR score for machine translation. Second, we observe that the learning-to-search approach benefits from choosing the costs related to the test metrics. Finally, we study the effect of different learning objectives and find that the standard KL loss only learns several high-probability tokens and can be replaced with ranking objectives that target these tokens explicitly.

Includes members:
  Member "insertion based decoding with automatically inferred generation order":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blank language models")
  Member "on the weaknesses of reinforcement learning for neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "bridging the gap between training and inference for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "non monotonic sequential text generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blank language models")
  Member "beyond bleu training neural machine translation with semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "on nmt search errors and model errors cat got your tongue":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "optimal completion distillation for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach")
  Member "loss functions for multiset prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non monotonic sequential text generation")

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.LG(Machine Learning)