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: "experiments in fast autonomous gps denied quadrotor flight"
  Abstract: High speed navigation through unknown environments is a challenging problem in robotics. It requires fast computation and tight integration of all the subsystems on the robot such that the latency in the perception-action loop is as small as possible. Aerial robots add a limitation of payload capacity, which restricts the amount of computation that can be carried onboard. This requires efficient algorithms for each component in the navigation system. #R##N#In this paper, we describe our quadrotor system which is able to smoothly navigate through mixed indoor and outdoor environments and is able to fly at speeds of more than 18 m/s. We provide an overview of our system and details about the specific component technologies that enable the high speed navigation capability of our platform. We demonstrate the robustness of our system through high speed autonomous flights and navigation through a variety of obstacle rich environments.

Connected through hyperedge HE-1 (induced by source paper "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "deep drone racing learning agile flight in dynamic environments"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "gibson env real world perception for embodied agents"
    - "ultra low power deep learning powered autonomous nano drones"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-2 (induced by source paper "evdodgenet deep dynamic obstacle dodging with event cameras"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "event based vision a survey"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-3 (induced by source paper "low latency visual slam with appearance enhanced local map building"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "appearance based landmark selection for efficient long term visual localization"
    - "ldso direct sparse odometry with loop closure"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

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)