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 "reinforcement learning with probabilistically complete exploration"):
  Abstract: Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in the sparse reward case, where they can do no better than to explore in all directions until the first positive rewards are found. To mitigate this, we propose Rapidly Randomly-exploring Reinforcement Learning (R3L). We formulate exploration as a search problem and leverage widely-used planning algorithms such as Rapidly-exploring Random Tree (RRT) to find initial solutions. These solutions are used as demonstrations to initialize a policy, then refined by a generic RL algorithm, leading to faster and more stable convergence. We provide theoretical guarantees of R3L exploration finding successful solutions, as well as bounds for its sampling complexity. We experimentally demonstrate the method outperforms classic and intrinsic exploration techniques, requiring only a fraction of exploration samples and achieving better asymptotic performance.

Includes members:
  Member "go explore a new approach for hard exploration problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation")
  Member "learning to plan via neural exploration exploitation trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "sim to real transfer of robotic control with dynamics randomization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "dora the explorer directed outreaching reinforcement action selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimistic exploration even with a pessimistic initialisation")
  Member "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "behavioral cloning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "parameter space noise for exploration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effective diversity in population based reinforcement learning")

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)