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 "learning reward functions by integrating human demonstrations and preferences"):
  Abstract: Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses expert demonstrations, and preference-based learning, which iteratively queries the user for her preferences between trajectories. In robotics however, IRL often struggles because it is difficult to get high-quality demonstrations; conversely, preference-based learning is very inefficient since it attempts to learn a continuous, high-dimensional function from binary feedback. We propose a new framework for reward learning, DemPref, that uses both demonstrations and preference queries to learn a reward function. Specifically, we (1) use the demonstrations to learn a coarse prior over the space of reward functions, to reduce the effective size of the space from which queries are generated; and (2) use the demonstrations to ground the (active) query generation process, to improve the quality of the generated queries. Our method alleviates the efficiency issues faced by standard preference-based learning methods and does not exclusively depend on (possibly low-quality) demonstrations. In numerical experiments, we find that DemPref is significantly more efficient than a standard active preference-based learning method. In a user study, we compare our method to a standard IRL method; we find that users rated the robot trained with DemPref as being more successful at learning their desired behavior, and preferred to use the DemPref system (over IRL) to train the robot.

Includes members:
  Member "risk aware active inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "an efficient generalized bellman update for cooperative inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non cooperative inverse reinforcement learning")
  Member "simplifying reward design through divide and conquer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy based continuous inverse optimal control")
  Member "reward learning from human preferences and demonstrations in atari":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "batch active preference based learning of reward functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interactive robot training for non markov tasks")
  Member "inverse reward design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "one shot imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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.RO(Robotics)