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 "correct and memorize learning to translate from interactive revisions"):
  Abstract: State-of-the-art machine translation models are still not on par with human translators. Previous work takes human interactions into the neural machine translation process to obtain improved results in target languages. However, not all model-translation errors are equal -- some are critical while others are minor. In the meanwhile, the same translation mistakes occur repeatedly in a similar context. To solve both issues, we propose CAMIT, a novel method for translating in an interactive environment. Our proposed method works with critical revision instructions, therefore allows human to correct arbitrary words in model-translated sentences. In addition, CAMIT learns from and softly memorizes revision actions based on the context, alleviating the issue of repeating mistakes. Experiments in both ideal and real interactive translation settings demonstrate that our proposed \method enhances machine translation results significantly while requires fewer revision instructions from human compared to previous methods.

Includes members:
  Member "active learning for interactive neural machine translation of data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "selection via proxy efficient data selection for deep learning")
  Member "fast lexically constrained decoding with dynamic beam allocation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards understanding neural machine translation with word importance")
  Member "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")
  Member "learning to discriminate noises for incorporating external information in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "neural machine translation decoding with terminology constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "commongen a constrained text generation dataset towards generative commonsense reasoning")
  Member "online learning for neural machine translation post editing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interactive predictive neural machine translation through reinforcement and imitation")
  Member "learning to remember translation history with a continuous cache":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual denoising pre training 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)