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: "an efficient variational approximation of the best fitting multi bernoulli filter"
  Abstract: The joint probabilistic data association (JPDA) filter is a popular tracking methodology for problems involving well-spaced targets, but it is rarely applied in problems with closely spaced targets due to its complexity in these cases, and due to the well-known phenomenon of coalescence. This paper addresses these difficulties using random finite sets (RFSs) and variational inference, deriving a highly tractable, approximate method for obtaining the multi-Bernoulli distribution that minimizes the set Kullback–Leibler (KL) divergence from the true posterior, working within the RFS framework to incorporate uncertainty in target existence. The derivation is interpreted as an application of expectation-maximization (EM), where the missing data is the correspondence of Bernoulli components (i.e., tracks) under each data association hypothesis. The missing data is shown to play an identical role to the selection of an ordered distribution in the same ordered family in the set JPDA algorithm. Subsequently, a special case of the proposed method is utilized to provide an efficient approximation of the minimum mean optimal subpattern assignment estimator. The performance of the proposed methods is demonstrated in challenging scenarios in which up to twenty targets come into close proximity.

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 "trajectory phd and cphd filters"):
  with members:
    - "generalized optimal sub pattern assignment metric"
    - "multiple target tracking based on sets of trajectories"
    - "a metric on the space of finite sets of trajectories for evaluation of multi target tracking algorithms"

Connected through hyperedge HE-3 (induced by source paper "poisson multi bernoulli mixture filter direct derivation and implementation"):
  with members:
    - "generalized optimal sub pattern assignment metric"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"

Connected through hyperedge HE-4 (induced by source paper "multisensor poisson multi bernoulli filter for joint target sensor state tracking"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "multiple scan data association by convex variational inference"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"

Connected through hyperedge HE-5 (induced by source paper "generalized optimal sub pattern assignment metric"):
  with members:
    - "approximate evaluation of marginal association probabilities with belief propagation"

Connected through hyperedge HE-6 (induced by source paper "multiple scan data association by convex variational inference"):
  with members:
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "loopy belief propagation for approximate inference an empirical study"
    - "the bethe permanent of a nonnegative matrix"
    - "a new class of upper bounds on the log partition function"
    - "approximating the permanent with fractional belief propagation"
    - "merging belief propagation and the mean field approximation a free energy approach"

Connected through hyperedge HE-7 (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"
    - "extended object tracking with random hypersurface models"

Connected through hyperedge HE-8 (induced by source paper "a metric on the space of finite sets of trajectories for evaluation of multi target tracking algorithms"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "generalized optimal sub pattern assignment metric"
    - "multiple target tracking based on sets of trajectories"
    - "a metric for sets of trajectories that is practical and mathematically consistent"

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)