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 "optimizing data usage via differentiable rewards"):
  Abstract: To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems. Similarly, a machine learning model could potentially be trained better with a scorer that "adapts" to its current learning state and estimates the importance of each training data instance. Training such an adaptive scorer efficiently is a challenging problem; in order to precisely quantify the effect of a data instance at a given time during the training, it is typically necessary to first complete the entire training process. To efficiently optimize data usage, we propose a reinforcement learning approach called Differentiable Data Selection (DDS). In DDS, we formulate a scorer network as a learnable function of the training data, which can be efficiently updated along with the main model being trained. Specifically, DDS updates the scorer with an intuitive reward signal: it should up-weigh the data that has a similar gradient with a dev set upon which we would finally like to perform well. Without significant computing overhead, DDS delivers strong and consistent improvements over several strong baselines on two very different tasks of machine translation and image classification.

Includes members:
  Member "revisiting low resource neural machine translation a case study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation")
  Member "target conditioned sampling optimizing data selection for multilingual neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pre training via leveraging assisting languages and data selection for neural machine translation")
  Member "self supervised generalisation with meta auxiliary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to generalize one sample at a time with self supervision")
  Member "reinforcement learning based curriculum optimization for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self regulated interactive sequence to sequence learning")
  Member "competence based curriculum learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "learning a multitask curriculum for neural machine translation")
  Member "multilingual neural machine translation with soft decoupled encoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)