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 "option compatible reward inverse reinforcement learning"):
  Abstract: Reinforcement learning with complex tasks is a challenging problem. Often, expert demonstrations of complex multitasking operations are required to train agents. However, it is difficult to design a reward function for given complex tasks. In this paper, we solve a hierarchical inverse reinforcement learning (IRL) problem within the framework of options. A gradient method for parametrized options is used to deduce a defining equation for the Q-feature space, which leads to a reward feature space. Using a second-order optimality condition for option parameters, an optimal reward function is selected. Experimental results in both discrete and continuous domains confirm that our segmented rewards provide a solution to the IRL problem for multitasking operations and show good performance and robustness against the noise created by expert demonstrations.

Includes members:
  Member "composing meta policies for autonomous driving using hierarchical deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "multi level discovery of deep options":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation")
  Member "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions")
  Member "the option critic architecture":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "guided cost learning deep inverse optimal control via policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "unsupervised perceptual rewards for imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "hierarchical reinforcement learning with the maxq value function decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")

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)