human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies"
  Abstract: This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference. Video: this https URL

Connected through hyperedge HE-1 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation"):
  with members:
    - "long range indoor navigation with prm rl"
    - "r maddpg for partially observable environments and limited communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "counterfactual multi agent policy gradients"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning with probabilistically complete exploration"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "learning to plan via neural exploration exploitation trees"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "behavioral cloning from observation"
    - "parameter space noise for exploration"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "defensive escort teams via multi agent deep reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "high dimensional continuous control using generalized advantage estimation"

Question: Please tell me which class the center node 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.RO(Robotics)