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 "optimal completion distillation for sequence learning"):
  Abstract: We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pretraining or joint optimization with conditional log-likelihood. Given a partial sequence generated by the model, we first identify the set of optimal suffixes that minimize the total edit distance, using an efficient dynamic programming algorithm. Then, for each position of the generated sequence, we use a target distribution that puts equal probability on the first token of all the optimal suffixes. OCD achieves the state-of-the-art performance on end-to-end speech recognition, on both Wall Street Journal and Librispeech datasets, achieving $9.3\%$ WER and $4.5\%$ WER respectively.

Includes members:
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer")
  Member "truncated horizon policy search combining reinforcement learning imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "token level and sequence level loss smoothing for rnn language models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rewe regressing word embeddings for regularization of neural machine translation systems")
  Member "switchout an efficient data augmentation algorithm for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised data augmentation for consistency training")
  Member "convergence of value aggregation for imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on policy robot imitation learning from a converging supervisor")
  Member "softmax q distribution estimation for structured prediction a theoretical interpretation for raml":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding knowledge distillation in non autoregressive machine translation")
  Member "improved training of end to end attention models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling up online speech recognition using convnets")

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)