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: "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
  Abstract: We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"

Connected through hyperedge HE-3 (induced by source paper "accelerating reinforcement learning for reaching using continuous curriculum learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "interactive open ended object affordance and grasp learning for robotic manipulation"
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "deep learning using rectified linear units relu"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "hindsight experience replay"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-4 (induced by source paper "danger aware adaptive composition of drl agents for self navigation"):
  with members:
    - "multi advisor reinforcement learning"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "uncertainty aware reinforcement learning for collision avoidance"

Connected through hyperedge HE-5 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments"):
  with members:
    - "policies modulating trajectory generators"
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "on evaluation of embodied navigation agents"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-6 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "object finding in cluttered scenes using interactive perception"
    - "deep value model predictive control"
    - "whole body mpc for a dynamically stable mobile manipulator"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "nonlinear model predictive guidance for fixed wing uavs using identified control augmented dynamics"

Connected through hyperedge HE-7 (induced by source paper "map less navigation a single drl based controller for robots with varied dimensions"):
  with members:
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning with stochastic guidance for navigation"
    - "addressing function approximation error in actor critic methods"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "robot navigation with map based deep reinforcement learning"):
  with members:
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "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"
    - "socially aware motion planning with deep reinforcement learning"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "prioritized experience replay"

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)