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: "extended object tracking with random hypersurface models"
  Abstract: The Random Hypersurface Model (RHM) is introduced that allows for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes.

Connected through hyperedge HE-1 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "robust distributed fusion with labeled random finite sets"
    - "generalized optimal sub pattern assignment metric"
    - "multisensor poisson multi bernoulli filter for joint target sensor state tracking"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "extended object tracking introduction overview and applications"
    - "a metric on the space of finite sets of trajectories for evaluation of multi target tracking algorithms"

Connected through hyperedge HE-2 (induced by source paper "tracking the orientation and axes lengths of an elliptical extended object"):
  with members:
    - "extended object tracking introduction overview and applications"
    - "second order extended kalman filter for extended object and group tracking"

Connected through hyperedge HE-3 (induced by source paper "extended object tracking introduction overview and applications"):
  with members:
    - "generalized optimal sub pattern assignment metric"
    - "second order extended kalman filter for extended object and group tracking"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "bayesian inference via approximation of log likelihood for priors in exponential family"
    - "an efficient variational approximation of the best fitting multi bernoulli filter"

Connected through hyperedge HE-4 (induced by source paper "second order extended kalman filter for extended object and group tracking"):
  with members:
    - "extended object tracking introduction overview and applications"
    - "a new perspective and extension of the gaussian filter"

Connected through hyperedge HE-5 (induced by source paper "recursive bayesian filtering in circular state spaces"):
  with members:
    - "recursive estimation of orientation based on the bingham distribution"

Connected through hyperedge HE-6 (induced by source paper "the smart sampling kalman filter with symmetric samples"):
  with members:
    - "the kernel sme filter for multiple target tracking"

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.SY(Systems and Control)