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 "parameter sharing in coagent networks"):
  Abstract: In this paper, we aim to prove the theorem that generalizes the Coagent Network Policy Gradient Theorem (Kostas et. al., 2019) to the context where parameters are shared among the function approximators involved. This provides the theoretical foundation to use any pattern of parameter sharing and leverage the freedom in the graph structure of the network to possibility exploit relational bias in a given task. As another application, we will apply our result to give a more intuitive proof for the Hierarchical Option Critic Policy Gradient Theorem, first shown in (Riemer et. al., 2019).

Includes members:
  Member "on the role of weight sharing during deep option learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical average reward policy gradient algorithms")
  Member "reinforcement learning without backpropagation or a clock":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "routing networks and the challenges of modular and compositional computation")
  Member "learning abstract options":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hierarchical teaching policies for cooperative agents")
  Member "the option critic architecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")

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)