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 "active inverse reward design"):
  Abstract: Reward design, the problem of selecting an appropriate reward function for an AI system, is both critically important, as it encodes the task the system should perform, and challenging, as it requires reasoning about and understanding the agent's environment in detail. As a result, system designers often iterate on the reward function in a trial-and-error process to get their desired behavior. We propose structuring this process as a series of reward design queries, where we actively select the set of reward functions available to the designer. We query with two types of sets: discrete queries, where the system designer chooses from a small set of reward functions, and feature queries, where the system queries the designer for weights on a small subset of features. After each query, we use inverse reward design (IRD) (Hadfield-Menell et al., 2017) to update the distribution over the true reward function from the observed proxy reward function chosen by the designer. Compared to vanilla IRD, we find that our approach not only decreases the uncertainty about the true reward, but also greatly improves performance in unseen environments while only querying for reward functions in a single training environment.

Includes members:
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "never give up learning directed exploration strategies")
  Member "learning from richer human guidance augmenting comparison based learning with feature queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using human ratings for feedback control a supervised learning approach with application to rehabilitation robotics")
  Member "repeated inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action for better prediction")
  Member "inverse reward design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "concrete problems in ai safety":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "value iteration networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "april active preference learning based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")

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)