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 "better than demonstrator imitation learning via automatically ranked demonstrations"):
  Abstract: The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow for better-than-demonstrator performance, preferences over demonstrations may be difficult to obtain, and little is known theoretically about when such methods can be expected to successfully extrapolate beyond the performance of the demonstrator. To address these issues, we first contribute a sufficient condition for better-than-demonstrator imitation learning and provide theoretical results showing why preferences over demonstrations can better reduce reward function ambiguity when performing inverse reinforcement learning. Building on this theory, we introduce Disturbance-based Reward Extrapolation (D-REX), a ranking-based imitation learning method that injects noise into a policy learned through behavioral cloning to automatically generate ranked demonstrations. These ranked demonstrations are used to efficiently learn a reward function that can then be optimized using reinforcement learning. We empirically validate our approach on simulated robot and Atari imitation learning benchmarks and show that D-REX outperforms standard imitation learning approaches and can significantly surpass the performance of the demonstrator. D-REX is the first imitation learning approach to achieve significant extrapolation beyond the demonstrator's performance without additional side-information or supervision, such as rewards or human preferences. By generating rankings automatically, we show that preference-based inverse reinforcement learning can be applied in traditional imitation learning settings where only unlabeled demonstrations are available.

Includes members:
  Member "inverse reinforcement learning with multiple ranked experts":
    also connected through other hyperedges:
      (none)
  Member "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "imitation learning from imperfect demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vild variational imitation learning with diverse quality demonstrations")
  Member "a review of robot learning for manipulation challenges representations and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "risk aware active inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "reinforcement learning from imperfect demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "ensembledagger a bayesian approach to safe imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hg dagger interactive imitation learning with human experts")

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)