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: "observability of strapdown ins alignment a global perspective"
  Abstract: Alignment of the strapdown inertial navigation system (INS) has strong nonlinearity, even worse when maneuvers, e.g., tumbling techniques, are employed to improve the alignment. There is no general rule to attack the observability of a nonlinear system, so most previous works addressed the observability of the corresponding linearized system by implicitly assuming that the original nonlinear system and the linearized one have identical observability characteristics. Strapdown INS alignment is a nonlinear system that has its own characteristics. Using the inherent properties of strapdown INS, e.g., the attitude evolution on the SO(3) manifold, we start from the basic definition and develop a global and constructive approach to investigate the observability of strapdown INS static and tumbling alignment, highlighting the effects of the attitude maneuver on observability. We prove that strapdown INS alignment, considering the unknown constant sensor biases, will be completely observable if the strapdown INS is rotated successively about two different axes and will be nearly observable for finite known unobservable states (no more than two) if it is rotated about a single axis. Observability from a global perspective provides us with insights into and a clearer picture of the problem, shedding light on previous theoretical results on strapdown INS alignment that were not comprehensive or consistent. The reporting of inconsistencies calls for a review of all linearization-based observability studies in the vast literature. Extensive simulations with constructed ideal observers and an extended Kalman filter are carried out, and the numerical results accord with the analysis. The conclusions can also assist in designing the optimal tumbling strategy and the appropriate state observer in practice to maximize the alignment performance.

Connected through hyperedge HE-1 (induced by source paper "gyroscope calibration via magnetometer"):
  with members:
    - "magnetometer calibration using inertial sensors"
    - "dynamic magnetometer calibration and alignment to inertial sensors by kalman filtering"
    - "on calibration of three axis magnetometer"

Connected through hyperedge HE-2 (induced by source paper "optimization based alignment for strapdown inertial navigation system comparison and extension"):
  with members:
    - "a new technique for ins gnss attitude and parameter estimation using online optimization"
    - "velocity position integration formula part i application to in flight coarse alignment"
    - "velocity position integration formula part ii application to strapdown inertial navigation computation"

Connected through hyperedge HE-3 (induced by source paper "dynamic magnetometer calibration and alignment to inertial sensors by kalman filtering"):
  with members:
    - "magnetometer calibration using inertial sensors"
    - "on calibration of three axis magnetometer"

Connected through hyperedge HE-4 (induced by source paper "underwater doppler navigation with self calibration"):
  with members:
    - "a new technique for ins gnss attitude and parameter estimation using online optimization"
    - "velocity position integration formula part i application to in flight coarse alignment"
    - "velocity position integration formula part ii application to strapdown inertial navigation computation"

Connected through hyperedge HE-5 (induced by source paper "versatile land navigation using inertial sensors and odometry self calibration in motion alignment and positioning"):
  with members:
    - "velocity position integration formula part i application to in flight coarse alignment"

Connected through hyperedge HE-6 (induced by source paper "velocity position integration formula part ii application to strapdown inertial navigation computation"):
  with members:
    - "velocity position integration formula part i application to in flight coarse alignment"

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)