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: "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
  Abstract: We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling-based path planning with reinforcement learning (RL) agents. The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology, while the sampling-based planners provide an approximate map of the space of possible configurations of the robot from which collision-free trajectories feasible for the RL agents can be identified. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces. We evaluate PRM-RL on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. These evaluations included both simulated environments and on-robot tests. Our results show improvement in navigation task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 meters long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1000 meters without violating the task constraints in an environment 63 million times larger than used in training.

Connected through hyperedge HE-1 (induced by source paper "adjust planning strategies to accommodate reinforcement learning agents"):
  with members:
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "semi parametric topological memory for navigation"
    - "a distributional perspective on reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "value iteration networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "search on the replay buffer bridging planning and reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "learning latent plans from play"
    - "the laplacian in rl learning representations with efficient approximations"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "learning navigation behaviors end to end with autorl"

Connected through hyperedge HE-3 (induced by source paper "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "towards characterizing divergence in deep q learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"

Connected through hyperedge HE-4 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"

Connected through hyperedge HE-5 (induced by source paper "long range indoor navigation with prm rl"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deepnav learning to navigate large cities"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

Connected through hyperedge HE-6 (induced by source paper "how to pick the domain randomization parameters for sim to real transfer of reinforcement learning policies"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "policies modulating trajectory generators"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "supervised policy update"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-7 (induced by source paper "hrl4in hierarchical reinforcement learning for interactive navigation with mobile manipulators"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"

Connected through hyperedge HE-8 (induced by source paper "learning resilient behaviors for navigation under uncertainty environments"):
  with members:
    - "a general framework for uncertainty estimation in deep learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to drive in a day"

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.AI(Artificial Intelligence)