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 "accelerating reinforcement learning with suboptimal guidance"):
  Abstract: Reinforcement Learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less random fashion for any learning signals. For control problems, we often have some controller readily available which might be suboptimal but nevertheless solves the problem to some degree. This controller can be used to guide the initial exploration phase of the learning controller towards reward yielding states, reducing the time before refinement of a viable policy can be initiated. In our work, the agent is guided through an auxiliary behaviour cloning loss which is made conditional on a Q-filter, i.e. it is only applied in situations where the critic deems the guiding controller to be better than the agent. The Q-filter provides a natural way to adjust the guidance throughout the training process, allowing the agent to exceed the guiding controller in a manner that is adaptive to the task at hand and the proficiency of the guiding controller. The contribution of this paper lies in identifying shortcomings in previously proposed implementations of the Q-filter concept, and in suggesting some ways these issues can be mitigated. These modifications are tested on the OpenAI Gym Fetch environments, showing clear improvements in adaptivity and yielding increased performance in all robotic environments tested.

Includes members:
  Member "multi goal reinforcement learning challenging robotics environments and request for research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized hindsight for reinforcement learning")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "learning with training wheels speeding up training with a simple controller for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "danger aware adaptive composition of drl agents for self navigation")
  Member "hindsight experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "deeply aggrevated differentiable imitation learning for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "overcoming exploration in reinforcement learning with demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "continuous control with deep reinforcement learning":
    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.LG(Machine Learning)