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 a multitask curriculum for neural machine translation"):
  Abstract: Existing curriculum learning research in neural machine translation (NMT) mostly focuses on a single final task such as selecting data for a domain or for denoising, and considers in-task example selection. This paper studies the data selection problem in multitask setting. We present a method to learn a multitask curriculum on a single, diverse, potentially noisy training dataset. It computes multiple data selection scores for each training example, each score measuring how useful the example is to a certain task. It uses Bayesian optimization to learn a linear weighting of these per-instance scores, and then sorts the data to form a curriculum. We experiment with three domain translation tasks: two specific domains and the general domain, and demonstrate that the learned multitask curriculum delivers results close to individually optimized models and brings solid gains over no curriculum training, across all test sets.

Includes members:
  Member "reinforcement learning based curriculum optimization for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing data usage via differentiable rewards")
  Member "curriculum learning for domain adaptation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation")
  Member "competence based curriculum learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to inpaint by progressively growing the mask regions")
  Member "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing data usage via differentiable rewards")
  Member "denoising neural machine translation training with trusted data and online data selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "curriculum learning for domain adaptation in neural machine translation")
  Member "an empirical exploration of curriculum learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking")
  Member "subword regularization improving neural network translation models with multiple subword candidates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fastai a layered api for deep learning")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)