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: "modeling and interpolation of the ambient magnetic field by gaussian processes"
  Abstract: Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.

Connected through hyperedge HE-1 (induced by source paper "sleipnir deterministic and provably accurate feature expansion for gaussian process regression with derivatives"):
  with members:
    - "ares and mars adversarial and mmd minimizing regression for sdes"
    - "odin ode informed regression for parameter and state inference in time continuous dynamical systems"
    - "scaling gaussian process regression with derivatives"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"

Connected through hyperedge HE-2 (induced by source paper "using inertial sensors for position and orientation estimation"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "magnetometer calibration using inertial sensors"
    - "towards self calibrating inertial body motion capture"
    - "a primer on the differential calculus of 3d orientations"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "unscented orientation estimation based on the bingham distribution"
    - "robust inference for state space models with skewed measurement noise"
    - "recursive estimation of orientation based on the bingham distribution"

Connected through hyperedge HE-3 (induced by source paper "scalable magnetic field slam in 3d using gaussian process maps"):
  with members:
    - "using inertial sensors for position and orientation estimation"

Connected through hyperedge HE-4 (induced by source paper "inertial odometry on handheld smartphones"):
  with members:
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"
    - "cooperative localization by dual foot mounted inertial sensors and inter agent ranging"

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)