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: "graphical model approximations of random finite set filters"
  Abstract: Random finite sets (RFSs) has been a fruitful area of research in recent years, yielding new approximate filters such as the probability hypothesis density (PHD), cardinalised PHD (CPHD), and multiple target multi-Bernoulli (MeMBer). These new methods have largely been based on approximations that side-step the need for measurement-to-track association. Comparably, RFS methods that incorporate data association, such as Morelande and Challa's (M-C) method, have received little attention. This paper provides a RFS algorithm that incorporates data association similarly to the M-C method, but retains computational tractability via a recently developed approximation of marginal association weights. We describe an efficient method for resolving the track coalescence phenomenon which is problematic for joint probabilistic data association (JPDA) and related methods (including M-C). The method utilises a network flow optimisation, and thus is tractable for large numbers of targets. Finally, our derivation also shows that it is natural for the multi-target density to incorporate both a Poisson point process (PPP) component (representing targets that have never been detected) and a multi-Bernoulli component (representing targets under track). We describe a method of recycling, in which tracks with a low probability existence are transferred from the multi-Bernoulli component to the PPP component, effectively yielding a hybrid of M-C and PHD.

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 "multisensor poisson multi bernoulli filtering with uncertain sensor states"):
  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"
    - "approximate evaluation of marginal association probabilities with belief propagation"

Connected through hyperedge HE-3 (induced by source paper "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"):
  with members:
    - "an efficient variational approximation of the best fitting multi bernoulli filter"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "the bethe permanent of a nonnegative matrix"
    - "hybrid poisson and multi bernoulli filters"
    - "approximating the permanent with belief propagation"
    - "belief propagation and beyond for particle tracking"

Connected through hyperedge HE-4 (induced by source paper "hybrid poisson and multi bernoulli filters"):
  with members:
    - "the bethe permanent of a nonnegative matrix"

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)