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 "risk sensitive inverse reinforcement learning via semi and non parametric methods"):
  Abstract: The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for a human's risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk-neutral to worst-case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human's underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with ten human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk-averse to risk-neutral in a data-efficient manner. Moreover, comparisons of the Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.

Includes members:
  Member "how should a robot assess risk towards an axiomatic theory of risk in robotics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning based distributionally robust motion control with gaussian processes")
  Member "guided cost learning deep inverse optimal control via policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "risk sensitive and robust decision making a cvar optimization approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wasserstein distributionally robust motion control for collision avoidance using conditional value at risk")
  Member "maximum entropy deep inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "cumulative prospect theory meets reinforcement learning prediction and control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "planning under risk and uncertainty based on prospect theoretic models")
  Member "risk sensitive reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully bayesian recurrent neural networks for safe reinforcement learning")
  Member "computational rationalization the inverse equilibrium problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "continuous inverse optimal control with locally optimal examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")

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.AI(Artificial Intelligence)