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 "extended object tracking introduction overview and applications"):
  Abstract: This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.

Includes members:
  Member "generalized optimal sub pattern assignment metric":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking")
  Member "second order extended kalman filter for extended object and group tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tracking the orientation and axes lengths of an elliptical extended object")
  Member "marginal multi bernoulli filters rfs derivation of mht jipda and association based member":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking")
  Member "bayesian inference via approximation of log likelihood for priors in exponential family":
    also connected through other hyperedges:
      (none)
  Member "an efficient variational approximation of the best fitting multi bernoulli filter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking")
  Member "extended object tracking with random hypersurface models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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.CV(Computer Vision and Pattern Recognition)