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: "ultra wideband aided fast localization and mapping system"
  Abstract: This paper proposes an ultra-wideband (UWB) aided localization and mapping system that leverages on inertial sensor and depth camera. Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. A general framework is developed which consists of three parallel threads, two of which carry out the visualinertial odometry (VIO) and UWB localization respectively. The other mapping thread integrates visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that a bundle adjustment is performed to provide robust trajectory estimation. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments.

Connected through hyperedge HE-1 (induced by source paper "uwb based system for uav localization in gnss denied environments characterization and dataset"):
  with members:
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "accurate 3d localization for mav swarms by uwb and imu fusion"
    - "robust target relative localization with ultra wideband ranging and communication"
    - "uwb lidar fusion for cooperative range only slam"
    - "an integrated localization navigation scheme for distance based docking of uavs"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information"

Connected through hyperedge HE-2 (induced by source paper "graph optimization approach to range based localization"):
  with members:
    - "kervolutional neural networks"
    - "kernel cross correlator"
    - "non iterative slam"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-3 (induced by source paper "kervolutional neural networks"):
  with members:
    - "decoupled networks"
    - "correlation flow robust optical flow using kernel cross correlators"
    - "kernel cross correlator"
    - "non linear convolution filters for cnn based learning"
    - "non iterative slam"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "robust target relative localization with ultra wideband ranging and communication"):
  with members:
    - "correlation flow robust optical flow using kernel cross correlators"
    - "kernel cross correlator"
    - "3 d reciprocal collision avoidance on physical quadrotor helicopters with on board sensing for relative positioning"

Connected through hyperedge HE-5 (induced by source paper "an integrated localization navigation scheme for distance based docking of uavs"):
  with members:
    - "robust target relative localization with ultra wideband ranging and communication"
    - "uav circumnavigating an unknown target under a gps denied environment with range only measurements"

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)