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 "reward conditioned policies"):
  Abstract: Reinforcement learning offers the promise of automating the acquisition of complex behavioral skills. However, compared to commonly used and well-understood supervised learning methods, reinforcement learning algorithms can be brittle, difficult to use and tune, and sensitive to seemingly innocuous implementation decisions. In contrast, imitation learning utilizes standard and well-understood supervised learning methods, but requires near-optimal expert data. Can we learn effective policies via supervised learning without demonstrations? The main idea that we explore in this work is that non-expert trajectories collected from sub-optimal policies can be viewed as optimal supervision, not for maximizing the reward, but for matching the reward of the given trajectory. By then conditioning the policy on the numerical value of the reward, we can obtain a policy that generalizes to larger returns. We show how such an approach can be derived as a principled method for policy search, discuss several variants, and compare the method experimentally to a variety of current reinforcement learning methods on standard benchmarks.

Includes members:
  Member "hindsight credit assignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variance reduced advantage estimation with δ hindsight credit assignment")
  Member "diagnosing bottlenecks in deep q learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")
  Member "learning to reach goals without reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "advantage weighted regression simple and scalable off policy reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "training agents using upside down reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "reinforcement learning upside down don t predict rewards just map them to actions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training agents using upside down reinforcement learning")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")

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)