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: "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information"
  Abstract: Relative positioning between multiple mobile users is essential for many applications, such as search and rescue in disaster areas or human social interaction. Inertial-measurement unit (IMU) is promising to determine the change of position over short periods of time, but it is very sensitive to error accumulation over long term run. By equipping the mobile users with ranging unit, e.g. ultra-wideband (UWB), it is possible to achieve accurate relative positioning by trilateration-based approaches. As compared to vision or laser-based sensors, the UWB does not need to be with in line-of-sight and provides accurate distance estimation. However, UWB does not provide any bearing information and the communication range is limited, thus UWB alone cannot determine the user location without any ambiguity. In this paper, we propose an approach to combine IMU inertial and UWB ranging measurement for relative positioning between multiple mobile users without the knowledge of the infrastructure. We incorporate the UWB and the IMU measurement into a probabilistic-based framework, which allows to cooperatively position a group of mobile users and recover from positioning failures. We have conducted extensive experiments to demonstrate the benefits of incorporating IMU inertial and UWB ranging measurements.

Connected through hyperedge HE-1 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (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"
    - "ultra wideband aided fast localization and mapping system"

Connected through hyperedge HE-3 (induced by source paper "collaborative slam based on wifi fingerprint similarity and motion information"):
  with members:
    - "simultaneous merging multiple grid maps using the robust motion averaging"
    - "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"
    - "modern wlan fingerprinting indoor positioning methods and deployment challenges"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-4 (induced by source paper "rf backscatter based state estimation for micro aerial vehicles"):
  with members:
    - "imgsensingnet uav vision guided aerial ground air quality sensing system"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "lora backscatter enabling the vision of ubiquitous connectivity"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "oxiod the dataset for deep inertial odometry"):
  with members:
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "lstm a search space odyssey"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "on manifold preintegration for real time visual inertial odometry"
    - "adam a method for stochastic optimization"

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.NI(Networking and Internet Architecture)