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: "combining optimal control and learning for visual navigation in novel environments"
  Abstract: Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through on-board sensors on the robot. In this work, we address this short-coming by coupling model-based control with learning-based perception. The learning-based perception module produces a series of waypoints that guide the robot to the goal via a collision-free path. These waypoints are used by a model-based planner to generate a smooth and dynamically feasible trajectory that is executed on the physical system using feedback control. Our experiments in simulated real-world cluttered environments and on an actual ground vehicle demonstrate that the proposed approach can reach goal locations more reliably and efficiently in novel environments as compared to purely geometric mapping-based or end-to-end learning-based alternatives. Our approach does not rely on detailed explicit 3D maps of the environment, works well with low frame rates, and generalizes well from simulation to the real world. Videos describing our approach and experiments are available on the project website.

Connected through hyperedge HE-1 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "learning to follow directions in street view"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "visual place recognition for aerial robotics exploring accuracy computation trade off for local image descriptors"
    - "cross view policy learning for street navigation"
    - "automatic coverage selection for surface based visual localization"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-2 (induced by source paper "learning to move with affordance maps"):
  with members:
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"

Connected through hyperedge HE-3 (induced by source paper "learning view and target invariant visual servoing for navigation"):
  with members:
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "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-4 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "emi exploration with mutual information"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "socially aware motion planning with deep reinforcement learning"

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"
    - "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"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-6 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "conceptualization of object compositions using persistent homology"
    - "admissible abstractions for near optimal task and motion planning"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "sampling based methods for factored task and motion planning"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "6 dof object pose from semantic keypoints"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-7 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path"):
  with members:
    - "learning robotic assembly from cad"
    - "learning navigation behaviors end to end with autorl"
    - "state representation learning for control an overview"
    - "motion planning networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "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"
    - "deeply informed neural sampling for robot motion planning"

Connected through hyperedge HE-8 (induced by source paper "learning to navigate using mid level visual priors"):
  with members:
    - "does computer vision matter for action"
    - "habitat a platform for embodied ai research"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "visual memory for robust path following"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"

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)