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 "deep bayesian reward learning from preferences"):
  Abstract: Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a learned policy. However, Bayesian IRL is computationally intractable for high-dimensional problems because each sample from the posterior requires solving an entire Markov Decision Process (MDP). While there exist non-Bayesian deep IRL methods, these methods typically infer point estimates of reward functions, precluding rigorous safety and uncertainty analysis. We propose Bayesian Reward Extrapolation (B-REX), a highly efficient, preference-based Bayesian reward learning algorithm that scales to high-dimensional, visual control tasks. Our approach uses successor feature representations and preferences over demonstrations to efficiently generate samples from the posterior distribution over the demonstrator's reward function without requiring an MDP solver. Using samples from the posterior, we demonstrate how to calculate high-confidence bounds on policy performance in the imitation learning setting, in which the ground-truth reward function is unknown. We evaluate our proposed approach on the task of learning to play Atari games via imitation learning from pixel inputs, with no access to the game score. We demonstrate that B-REX learns imitation policies that are competitive with a state-of-the-art deep imitation learning method that only learns a point estimate of the reward function. Furthermore, we demonstrate that samples from the posterior generated via B-REX can be used to compute high-confidence performance bounds for a variety of evaluation policies. We show that high-confidence performance bounds are useful for accurately ranking different evaluation policies when the reward function is unknown. We also demonstrate that high-confidence performance bounds may be useful for detecting reward hacking.

Includes members:
  Member "asking easy questions a user friendly approach to active reward learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "extending deep model predictive control with safety augmented value estimation from demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "beyond confidence regions tight bayesian ambiguity sets for robust mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "risk aware active inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "importance sampling policy evaluation with an estimated behavior policy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "a survey of inverse reinforcement learning challenges methods and progress":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations")

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)