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: "efficient probabilistic performance bounds for inverse reinforcement learning"
  Abstract: In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward function is unknown and only samples of expert behavior are given. We propose a sampling method based on Bayesian inverse reinforcement learning that uses demonstrations to determine practical high-confidence upper bounds on the $\alpha$-worst-case difference in expected return between any evaluation policy and the optimal policy under the expert's unknown reward function. We evaluate our proposed bound on both a standard grid navigation task and a simulated driving task and achieve tighter and more accurate bounds than a feature count-based baseline. We also give examples of how our proposed bound can be utilized to perform risk-aware policy selection and risk-aware policy improvement. Because our proposed bound requires several orders of magnitude fewer demonstrations than existing high-confidence bounds, it is the first practical method that allows agents that learn from demonstration to express confidence in the quality of their learned policy.

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"
    - "asking easy questions a user friendly approach to active reward learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations"):
  with members:
    - "inverse reinforcement learning with multiple ranked experts"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "ensembledagger a bayesian approach to safe imitation learning"

Connected through hyperedge HE-3 (induced by source paper "deep bayesian reward learning from preferences"):
  with members:
    - "asking easy questions a user friendly approach to active reward learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "teaching categories to human learners with visual explanations"
    - "machine teaching for inverse reinforcement learning algorithms and applications"

Connected through hyperedge HE-5 (induced by source paper "defining admissible rewards for high confidence policy evaluation"):
  with members:
    - "learning safe policies with expert guidance"
    - "safe policy learning from observations"
    - "constrained policy improvement for safe and efficient reinforcement learning"
    - "learning optimal policies from observational data"
    - "ai safety gridworlds"
    - "safe policy improvement with baseline bootstrapping"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "inverse reward design"

Connected through hyperedge HE-6 (induced by source paper "risk aware active inverse reinforcement learning"):
  with members:
    - "interactive learning from policy dependent human feedback"
    - "concrete problems in ai safety"
    - "risk sensitive and robust decision making a cvar optimization approach"

Connected through hyperedge HE-7 (induced by source paper "inverse reinforcement learning with multiple ranked experts"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"

Connected through hyperedge HE-8 (induced by source paper "machine teaching for inverse reinforcement learning algorithms and applications"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "an overview of machine teaching"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "enabling robots to communicate their objectives"
    - "preference based teaching"
    - "cooperative inverse reinforcement learning"
    - "successor features for transfer in reinforcement 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.AI(Artificial Intelligence)