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: "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
  Abstract: This letter was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the letter, we propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website to provide the service based on LFRL:  www.shared-robotics.com .

Connected through hyperedge HE-1 (induced by source paper "design and implementation of a novel precision irrigation robot based on an intelligent path planning algorithm"):
  with members:
    - "federated imitation learning a novel framework for cloud robotic systems with heterogeneous sensor data"
    - "a comprehensive survey on transfer learning"
    - "coupling of crop assignment and vehicle routing for harvest planning in agriculture"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "reinforcement learning based resource allocation in fog radio access networks for various iot environments"

Connected through hyperedge HE-3 (induced by source paper "driving reinforcement learning with models"):
  with members:
    - "control tutored reinforcement learning an application to the herding problem"
    - "model ensemble trust region policy optimization"
    - "data driven policy transfer with imprecise perception simulation"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a differentiable physics engine for deep learning in robotics"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-4 (induced by source paper "revocable federated learning a benchmark of federated forest"):
  with members:
    - "towards federated learning at scale system design"
    - "evaluating differentially private machine learning in practice"
    - "boosting privately privacy preserving federated extreme boosting for mobile crowdsensing"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "machine learning in compiler optimisation"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "switching convolutional neural network for crowd counting"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-5 (induced by source paper "federated imitation learning a novel framework for cloud robotic systems with heterogeneous sensor data"):
  with members:
    - "a comprehensive survey on transfer learning"
    - "meta learning a survey"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "one shot visual imitation learning via meta learning"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-6 (induced by source paper "federated transfer reinforcement learning for autonomous driving"):
  with members:
    - "federated machine learning concept and applications"
    - "universal successor representations for transfer reinforcement learning"
    - "virtual to real reinforcement learning for autonomous driving"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "federated imitation learning a privacy considered imitation learning framework for cloud robotic systems with heterogeneous sensor data"):
  with members:
    - "network offloading policies for cloud robotics a learning based approach"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "recent advances in imitation learning from observation"
    - "federated learning for mobile keyboard prediction"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-8 (induced by source paper "a robust stereo camera localization method with prior lidar map constrains"):
  with members:
    - "low cost gps aided lidar state estimation and map building"
    - "laser map aided visual inertial localization in changing environment"
    - "pcr pro 3d sparse and different scale point clouds registration and robust estimation of information matrix for pose graph slam"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"

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)