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 "hindsight trust region policy optimization"):
  Abstract: As reinforcement learning continues to drive machine intelligence beyond its conventional boundary, unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields. Motivated by the demand for an effective deep reinforcement learning algorithm that accommodates sparse reward environment, this paper presents Hindsight Trust Region Policy Optimization (Hindsight TRPO), a method that efficiently utilizes interactions in sparse reward conditions and maintains learning stability by restricting variance during the policy update process. Firstly, the hindsight methodology is expanded to TRPO, an advanced and efficient on-policy policy gradient method. Then, under the condition that the distributions are close, the KL-divergence is appropriately approximated by another $f$-divergence. Such approximation results in the decrease of variance during KL-divergence estimation and alleviates the instability during policy update. Experimental results on both discrete and continuous benchmark tasks demonstrate that Hindsight TRPO converges steadily and significantly faster than previous policy gradient methods. It achieves effective performances and high data-efficiency for training policies in sparse reward environments.

Includes members:
  Member "hierarchical reinforcement learning with hindsight":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning")
  Member "variance reduction for policy gradient with action dependent factorized baselines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for cyber security")
  Member "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "acceleration of actor critic deep reinforcement learning for visual grasping in clutter by state representation learning based on disentanglement of a raw input image")
  Member "data efficient deep reinforcement learning for dexterous manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "visual reinforcement learning with imagined goals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "multi goal reinforcement learning challenging robotics environments and request for research":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalized hindsight for reinforcement learning")
  Member "hierarchical imitation and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning hierarchical teaching policies for cooperative agents")
  Member "many goals reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling all goals updates in reinforcement learning using convolutional neural networks")

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)