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: "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
  Abstract: Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by the ambiguity set---the set of plausible transition probabilities---which is usually constructed as a multi-dimensional confidence region. Existing methods construct ambiguity sets as confidence regions using concentration inequalities which leads to overly conservative solutions. This paper proposes a new paradigm that can achieve better solutions with the same robustness guarantees without using confidence regions as ambiguity sets. To incorporate prior knowledge, our algorithms optimize the size and position of ambiguity sets using Bayesian inference. Our theoretical analysis shows the safety of the proposed method, and the empirical results demonstrate its practical promise.

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 "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "wasserstein robust reinforcement learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "compatible natural gradient policy search"
    - "non stationary markov decision processes a worst case approach using model based reinforcement learning"
    - "action robust reinforcement learning and applications in continuous control"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "deep lagrangian networks for end to end learning of energy based control for under actuated systems"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a bayesian approach to robust reinforcement learning"):
  with members:
    - "reward constrained policy optimization"
    - "tight bayesian ambiguity sets for robust mdps"
    - "soft robust actor critic policy gradient"
    - "the uncertainty bellman equation and exploration"
    - "deep robust kalman filter"
    - "reinforcement learning under model mismatch"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "distributionally robust counterpart in markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "optimizing norm bounded weighted ambiguity sets for robust mdps"):
  with members:
    - "tight bayesian ambiguity sets for robust mdps"

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.LG(Machine Learning)