human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multiobject fusion with minimum information loss"):
  Abstract: Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, the interest is in the fusion rule that dually minimizes the weighted information loss (WIL) and it turns out that such a fusion rule is consistent with the so-called linear opinion pool (LOP). However, the LOP cannot be directly applied to multiobject fusion since the resulting fused multiobject density (FMD), in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in the context of Bayesian multiobject filtering. In order to overcome such a difficulty, the principle of minimizing WIL is further exploited in that the optimal FMD in the same family of the local ones is looked for. Implementation issues relative to the proposed minimum WIL (MWIL) fusion rule are discussed. Finally, the performance of the MWIL rule is assessed via simulation experiments concerning distributed multitarget tracking over a wireless sensor network.

Includes members:
  Member "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "second order statistics analysis and comparison between arithmetic and geometric average fusion application to multi sensor target tracking")
  Member "second order statistics analysis and comparison between arithmetic and geometric average fusion application to multi sensor target tracking":
    also connected through other hyperedges:
      (none)
  Member "robust distributed fusion with labeled random finite sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking")
  Member "distributed smc phd fusion for partial arithmetic average consensus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion")
  Member "distributed fusion with multi bernoulli filter based on generalized covariance intersection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking")
  Member "the cauchy schwarz divergence for poisson point processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decentralized multi target tracking in urban environments overview and challenges")
  Member "consensus labeled random finite set filtering for distributed multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking")

Question: Please tell me which class the source paper 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)