human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "learning constraints from locally optimal demonstrations under cost function uncertainty"):
  with members:
    - "learning parametric constraints in high dimensions from demonstrations"
    - "constrained inverse optimal control with application to a human manipulation task"
    - "learning constraints from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "learning parametric constraints in high dimensions from demonstrations"):
  with members:
    - "predictive modeling by infinite horizon constrained inverse optimal control with application to a human manipulation task"
    - "learning constraints from demonstrations"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "repeated inverse reinforcement learning"
    - "safe exploration in finite markov decision processes with gaussian processes"

Connected through hyperedge HE-3 (induced by source paper "active learning for risk sensitive inverse reinforcement learning"):
  with members:
    - "carla an open urban driving simulator"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-4 (induced by source paper "risk sensitive generative adversarial imitation learning"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "rail risk averse imitation learning"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"
    - "policy gradient for coherent risk measures"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)