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 "manufacturing dispatching using reinforcement and transfer learning"):
  Abstract: Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can work in dynamic and stochastic environments, meaning it allows for quick response to new orders received and can work over a disparate set of shop floor settings. In this paper we address this problem of dispatching in manufacturing. Using reinforcement learning (RL), we propose a new design to formulate the shop floor state as a 2-D matrix, incorporate job slack time into state representation, and design lateness and tardiness rewards function for dispatching purpose. However, maintaining a separate RL model for each production line on a manufacturing shop floor is costly and often infeasible. To address this, we enhance our deep RL model with an approach for dispatching policy transfer. This increases policy generalization and saves time and cost for model training and data collection. Experiments show that: (1) our approach performs the best in terms of total discounted reward and average lateness, tardiness, (2) the proposed policy transfer approach reduces training time and increases policy generalization.

Includes members:
  Member "minimal support vector machine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks for failure prediction")
  Member "regularized singular value decomposition and application to recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks for failure prediction")
  Member "cross domain transfer in reinforcement learning using target apprentice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity")
  Member "a harmonic mean linear discriminant analysis for robust image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks for failure prediction")
  Member "accelerating deep learning with shrinkage and recall":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks for failure prediction")
  Member "kernel alignment inspired linear discriminant analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks for failure prediction")
  Member "analysis and modeling of social influence in high performance computing workloads":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks for failure prediction")

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)