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: "neural autonomous navigation with riemannian motion policy"
  Abstract: End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especially around generalization to unseen environments, is high. We introduce a novel image-based autonomous navigation technique that leverages in policy structure using the Riemannian Motion Policy (RMP) framework for deep learning of vehicular control. We design a deep neural network to predict control point RMPs of the vehicle from visual images, from which the optimal control commands can be computed analytically. We show that our network trained in the Gibson environment can be used for indoor obstacle avoidance and navigation on a real RC car, and our RMP representation generalizes better to unseen environments than predicting local geometry or predicting control commands directly.

Connected through hyperedge HE-1 (induced by source paper "badgr an autonomous self supervised learning based navigation system"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "deep visual mpc policy learning for navigation"
    - "composable action conditioned predictors flexible off policy learning for robot navigation"
    - "deep ordinal regression network for monocular depth estimation"
    - "semi parametric topological memory for navigation"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "learning to drive in a day"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-2 (induced by source paper "learning view and target invariant visual servoing for navigation"):
  with members:
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-3 (induced by source paper "technical report reactive semantic planning in unexplored semantic environments using deep perceptual feedback"):
  with members:
    - "reactive navigation in partially familiar planar environments using semantic perceptual feedback"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "yolov3 an incremental improvement"
    - "semi parametric topological memory for navigation"
    - "6 dof object pose from semantic keypoints"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-4 (induced by source paper "situational fusion of visual representation for visual navigation"):
  with members:
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "virtual to real learning to control in visual semantic segmentation"
    - "driving policy transfer via modularity and abstraction"
    - "learning to navigate in cities without a map"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (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"
    - "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"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-6 (induced by source paper "generating robust supervision for learning based visual navigation using hamilton jacobi reachability"):
  with members:
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-7 (induced by source paper "scaling local control to large scale topological navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "deep visual mpc policy learning for navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"

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)