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: "velocity position integration formula part ii application to strapdown inertial navigation computation"
  Abstract: Inertial navigation applications are usually referenced to a rotating frame. Consideration of the navigation reference frame rotation in the inertial navigation algorithm design is an important but so far less seriously treated issue, especially for super high-speed flying vehicles or the future ultraprecision navigation system of several meters per hour. A rigorous approach is proposed to tackle the issue of navigation frame rotation in velocity/position computation by use of the newly-devised velocity/position integration formulae in the Part I companion paper. The two integration formulae set a well-founded cornerstone for the velocity/position algorithms' design that makes the comprehension of the inertial navigation computation principle more accessible to practitioners, and different approximations to the integrals involved give birth to various velocity/position update algorithms. Two-sample velocity and position algorithms are derived to exemplify the design process. In the context of level-flight airplane examples, the derived algorithm is analytically and numerically compared with the typical algorithms that exist in the literature. The results throw light on the problems in existing algorithms and the potential benefits of the derived algorithm.

Connected through hyperedge HE-1 (induced by source paper "inavfiter next generation inertial navigation computation based on functional iteration"):
  with members:
    - "attitude reconstruction from inertial measurements quatfiter and its comparison with rodfiter"
    - "on inertial navigation and attitude initialization in polar areas"
    - "fast rodfiter for attitude reconstruction from inertial measurements"
    - "rodfiter attitude reconstruction from inertial measurement by functional iteration"
    - "on position translation vector"

Connected through hyperedge HE-2 (induced by source paper "next generation inertial navigation computation based on functional iteration"):
  with members:
    - "attitude reconstruction from inertial measurements quatfiter and its comparison with rodfiter"
    - "fast rodfiter for attitude reconstruction from inertial measurements"
    - "rodfiter attitude reconstruction from inertial measurement by functional iteration"
    - "on position translation vector"

Connected through hyperedge HE-3 (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"
    - "observability of strapdown ins alignment a global perspective"

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"
    - "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)