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 "inverse reinforcement learning via nonparametric spatio temporal subgoal modeling"):
  Abstract: Recent advances in the field of inverse reinforcement learning (IRL) have yielded sophisticated frameworks which relax the original modeling assumption that the behavior of an observed agent reflects only a single intention. Instead, the demonstration data is typically divided into parts, to account for the fact that different trajectories may correspond to different intentions, e.g., because they were generated by different domain experts. In this work, we go one step further: using the intuitive concept of subgoals, we build upon the premise that even a single trajectory can be explained more efficiently locally within a certain context than globally, enabling a more compact representation of the observed behavior. Based on this assumption, we build an implicit intentional model of the agent's goals to forecast its behavior in unobserved situations. The result is an integrated Bayesian prediction framework which provides smooth policy estimates that are consistent with the expert's plan and significantly outperform existing IRL solutions. Most notably, our framework naturally handles situations where the intentions of the agent change with time and classical IRL algorithms fail. In addition, due to its probabilistic nature, the model can be straightforwardly applied in an active learning setting to guide the demonstration process of the expert.

Includes members:
  Member "autonomous agents modelling other agents a comprehensive survey and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "boltzmann exploration done right":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "making sense of reinforcement learning and probabilistic inference")
  Member "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions")
  Member "bayesian reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "on the complexity of solving markov decision problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "apprenticeship learning using inverse reinforcement learning and gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning navigation costs from demonstration in partially observable environments")

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)