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: "multi bernoulli sensor control via minimization of expected estimation errors"
  Abstract: This paper presents a sensor-control method for choosing the best next state of the sensor(s), that provide(s) accurate estimation results in a multi-target tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy.

Connected through hyperedge HE-1 (induced by source paper "multi sensor control for multi object bayes filters"):
  with members:
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "sensor control for multi object tracking using labeled multi bernoulli filter"
    - "the cauchy schwarz divergence for poisson point processes"
    - "consensus labeled random finite set filtering for distributed multi object tracking"
    - "sensor management for multi target tracking via multi bernoulli filtering"
    - "on the finite time convergence of cyclic coordinate descent methods"

Connected through hyperedge HE-2 (induced by source paper "statistical information fusion for multiple view sensor data in multi object tracking"):
  with members:
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "the cauchy schwarz divergence for poisson point processes"
    - "sensor management for multi target tracking via multi bernoulli filtering"

Connected through hyperedge HE-3 (induced by source paper "distributed fusion with multi bernoulli filter based on generalized covariance intersection"):
  with members:
    - "distributed fusion of labeled multi object densities via label spaces matching"
    - "consensus labeled random finite set filtering for distributed multi object tracking"
    - "hybrid poisson and multi bernoulli filters"

Connected through hyperedge HE-4 (induced by source paper "multi sensor control for multi target tracking using cauchy schwarz divergence"):
  with members:
    - "the cauchy schwarz divergence for poisson point processes"
    - "consensus labeled random finite set filtering for distributed multi object tracking"
    - "sensor management for multi target tracking via multi bernoulli filtering"

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)