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: "asking easy questions a user friendly approach to active reward learning"
  Abstract: Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response; however, they do not consider how easy it will be for the human to answer! In this paper we explore an information gain formulation for optimally selecting questions that naturally account for the human's ability to answer. Our approach identifies questions that optimize the trade-off between robot and human uncertainty, and determines when these questions become redundant or costly. Simulations and a user study show our method not only produces easy questions, but also ultimately results in faster reward learning.

Connected through hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"

Connected through hyperedge HE-2 (induced by source paper "when humans aren t optimal robots that collaborate with risk aware humans"):
  with members:
    - "better than demonstrator imitation learning via automatically ranked demonstrations"
    - "enabling robots to infer how end users teach and learn through human robot interaction"
    - "ranking based reward extrapolation without rankings"
    - "controlling assistive robots with learned latent actions"
    - "learning reward functions by integrating human demonstrations and preferences"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "decision theory with resource bounded agents"
    - "capir collaborative action planning with intention recognition"

Connected through hyperedge HE-3 (induced by source paper "deep bayesian reward learning from preferences"):
  with members:
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
    - "risk aware active inverse reinforcement learning"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "playing hard exploration games by watching youtube"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "risk sensitive generative adversarial imitation learning"

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.RO(Robotics)