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 to copy for automatic post editing"):
  Abstract: Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved considerable performance gains by using neural networks, how to model the copying mechanism for APE remains a challenge. In this work, we propose a new method for modeling copying for APE. To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way. These representations are used to explicitly indicate which words in the system outputs should be copied, which is useful to help CopyNet (Gu et al., 2016) better generate post-edited translations. Experiments on the datasets of the WMT 2016-2017 APE shared tasks show that our approach outperforms all best published results.

Includes members:
  Member "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")
  Member "cross lingual language model pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "escape a large scale synthetic corpus for automatic post editing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simple and effective approach to automatic post editing with transfer learning")
  Member "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incorporating bert into neural machine translation")
  Member "modeling localness for self attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gret global representation enhanced transformer")
  Member "a shared attention mechanism for interpretation of neural automatic post editing systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "findings of the second workshop on neural machine translation and generation")
  Member "thumt an open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)