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: "long range indoor navigation with prm rl"
  Abstract: Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on-robot, guiding the robot along the shortest path where the agents are likely to succeed. Here we use Probabilistic Roadmaps (PRMs) as the sampling-based planner and AutoRL as the reinforcement learning method in the indoor navigation context. We evaluate the method in simulation for kinematic differential drive and kinodynamic car-like robots in several environments, and on-robot for differential-drive robots at two physical sites. Our results show PRM-RL with AutoRL is more successful than several baselines, is robust to noise, and can guide robots over hundreds of meters in the face of noise and obstacles in both simulation and on-robot, including over 3.3 kilometers of physical robot navigation.

Connected through hyperedge HE-1 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation"):
  with members:
    - "r maddpg for partially observable environments and limited communication"
    - "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies"
    - "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 "zero shot imitation learning from demonstrations for legged robot visual navigation"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning navigation subroutines from egocentric videos"
    - "universal planning networks"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "zero shot visual imitation"
    - "learning navigation behaviors end to end with autorl"

Connected through hyperedge HE-3 (induced by source paper "learning your way without map or compass panoramic target driven visual navigation"):
  with members:
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "habitat a platform for embodied ai research"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "learning to navigate in cities without a map"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-4 (induced by source paper "learned critical probabilistic roadmaps for robotic motion planning"):
  with members:
    - "lego leveraging experience in roadmap generation for sampling based planning"
    - "identifying critical regions for motion planning using auto generated saliency labels with convolutional neural networks"
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "the laplacian in rl learning representations with efficient approximations"
    - "learning implicit sampling distributions for motion planning"
    - "learning sampling distributions for robot motion planning"
    - "robust locally linear controllable embedding"

Connected through hyperedge HE-5 (induced by source paper "effects of a social force model reward in robot navigation based on deep reinforcement learning"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "reward shaping via meta learning"
    - "deep visual mpc policy learning for navigation"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "rrt colearn towards kinodynamic planning without numerical trajectory optimization"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "deep reinforcement learning with successor features for navigation across similar environments"

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)