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 lightweight and accurate localization algorithm using multiple inertial measurement units"
  Abstract: In this paper, a lightweight and accurate localization algorithm is proposed using measurements from multiple inertial measurement units (IMUs). IMU is a low-cost motion sensor which provides measurements on rotational velocity and gravity compensated linear acceleration of a moving platform, and it is widely used in localization problems. To date, most existing work that employs IMU sensor for localization focuses on algorithms or applications based on a single IMU. While this category of design yields acceptable accuracy and robustness for different use cases, the overall performance can be further improved by using multiple IMUs. To this end, we propose a lightweight and accurate algorithm for IMU-assisted localization, which is able to obtain noticeable performance gain without incurring additional computational cost. To achieve this, we first probabilistically map measurements from all IMUs onto a virtual IMU. This step is performed by stochastic estimation with least-squares estimators and probabilistic marginalization of inter-IMU rotational accelerations. Subsequently, propagation equations of both state vector and error state of the virtual IMU are also derived, which enables the use of the classical filter-based or optimization-based localization algorithms via the virtual IMU. Finally, results from both simulation and real-world tests are provided, which demonstrate that the proposed algorithm outperforms competing algorithms by noticeable margins.

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)