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 new technique for ins gnss attitude and parameter estimation using online optimization"
  Abstract: Integration of inertial navigation system (INS) and global navigation satellite system (GNSS) is usually implemented in engineering applications by way of Kalman-like filtering. This form of INS/GNSS integration is prone to attitude initialization failure, especially when the host vehicle is moving freely. This paper proposes an online constrained-optimization method to simultaneously estimate the attitude and other related parameters including GNSS antenna's lever arm and inertial sensor biases. This new technique benefits from self-initialization in which no prior attitude or sensor measurement noise information is required. Numerical results are reported to validate its effectiveness and prospect in high accurate INS/GNSS applications.

Connected through hyperedge HE-1 (induced by source paper "real time magnetometer disturbance estimation via online nonlinear programming"):
  with members:
    - "dynamic magnetometer calibration and alignment to inertial sensors by kalman filtering"

Connected through hyperedge HE-2 (induced by source paper "on inertial navigation and attitude initialization in polar areas"):
  with members:
    - "velocity position integration formula part i application to in flight coarse alignment"

Connected through hyperedge HE-3 (induced by source paper "semi analytic method for sins attitude and parameters online estimation"):
  with members:
    - "optimization based alignment for strapdown inertial navigation system comparison and extension"
    - "velocity position integration formula part i application to in flight coarse alignment"

Connected through hyperedge HE-4 (induced by source paper "optimization based alignment for strapdown inertial navigation system comparison and extension"):
  with members:
    - "velocity position integration formula part i application to in flight coarse alignment"
    - "velocity position integration formula part ii application to strapdown inertial navigation computation"
    - "observability of strapdown ins alignment a global perspective"

Connected through hyperedge HE-5 (induced by source paper "underwater doppler navigation with self calibration"):
  with members:
    - "velocity position integration formula part i application to in flight coarse alignment"
    - "velocity position integration formula part ii application to strapdown inertial navigation computation"
    - "observability of strapdown ins alignment a global perspective"

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)