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: "a general optimization based framework for local odometry estimation with multiple sensors"
  Abstract: Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift-free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU, LiDAR, etc) provide precise pose within a small region, while global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally drift-free localization in a large-scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation. Local estimations, produced by existing VO/VIO approaches, are fused with global sensors in a pose graph optimization. Within the graph optimization, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluate the performance of our system on public datasets and with real-world experiments. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization. Our implementations are open source\footnote{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "panoptic segmentation"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "visual odometry revisited what should be learnt"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-2 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "characterizing slam benchmarks and methods for the robust perception age"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "robust stereo visual inertial odometry for fast autonomous flight"

Connected through hyperedge HE-3 (induced by source paper "uwb based system for uav localization in gnss denied environments characterization and dataset"):
  with members:
    - "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"
    - "ultra wideband aided fast localization and mapping system"
    - "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information"

Connected through hyperedge HE-4 (induced by source paper "bidirectional trajectory computation for odometer aided visual inertial slam"):
  with members:
    - "visual inertial navigation a concise review"
    - "visual inertial localization for skid steering robots with kinematic constraints"
    - "localization for ground robots on manifold representation integration re parameterization and optimization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "tightly coupled monocular visual odometric slam using wheels and a mems gyroscope"

Connected through hyperedge HE-5 (induced by source paper "inertial only optimization for visual inertial initialization"):
  with members:
    - "fast and robust initialization for visual inertial slam"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "tightly coupled fusion of global positional measurements in optimization based visual inertial odometry"):
  with members:
    - "visual inertial navigation a concise review"
    - "visual inertial odometry of aerial robots"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-7 (induced by source paper "robust tightly coupled pose estimation based on monocular vision inertia and wheel speed"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "decentralized visual inertial uwb fusion for relative state estimation of aerial swarm"):
  with members:
    - "yolov3 an incremental improvement"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

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.CV(Computer Vision and Pattern Recognition)