human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning pose estimation for uav autonomous navigation and landing using visual inertial sensor data"):
  Abstract: In this work, we propose a robust network-in-the-loop control system that allows an Unmanned-Aerial-Vehicles to navigate and land autonomously on a desired target. To estimate the global pose of the aerial vehicle, we develop a deep neural network architecture for visual-inertial odometry, which provides a robust alternative to traditional techniques for autonomous navigation of Unmanned-Aerial-Vehicles. We first provide experimental results on the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% against the baseline. Second, we use Airsim, a simulator available as a plugin for UnrealEngine, to create new datasets of photorealistic images and inertial measurement to train and test our model. We finally integrate the proposed architecture for global localization with the Airsim closed-loop control system, and we provide simulation results for the autonomous landing of the aerial vehicle.

Includes members:
  Member "vins mono a robust and versatile monocular visual inertial state estimator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "se3 pose nets structured deep dynamics models for visuomotor planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action for better prediction")
  Member "vinet visual inertial odometry as a sequence to sequence learning problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "airsim high fidelity visual and physical simulation for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "visual servoing from deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning view and target invariant visual servoing for navigation")
  Member "image matching using sift surf brief and orb performance comparison for distorted images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimating a null model of scientific image reuse to support research integrity investigations")

Question: Please tell me which class the source paper 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.LG(Machine Learning)