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 "nonlinear compressive particle filtering"):
  Abstract: Many systems for which compressive sensing is used today are dynamical. The common approach is to neglect the dynamics and see the problem as a sequence of independent problems. This approach has two disadvantages. Firstly, the temporal dependency in the state could be used to improve the accuracy of the state estimates. Secondly, having an estimate for the state and its support could be used to reduce the computational load of the subsequent step. In the linear Gaussian setting, compressive sensing was recently combined with the Kalman filter to mitigate above disadvantages. In the nonlinear dynamical case, compressive sensing can not be used and, if the state dimension is high, the particle filter would perform poorly. In this paper we combine one of the most novel developments in compressive sensing, nonlinear compressive sensing, with the particle filter. We show that the marriage of the two is essential and that neither the particle filter or nonlinear compressive sensing alone gives a satisfying solution.

Includes members:
  Member "quadratic basis pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "pafimocs particle filtered modified cs and applications in visual tracking across illumination change":
    also connected through other hyperedges:
      (none)
  Member "sparsity constrained nonlinear optimization optimality conditions and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization")
  Member "compressed sensing with nonlinear observations and related nonlinear optimisation problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "kalman filtered compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "designing recurrent neural networks by unfolding an l1 l1 minimization algorithm")
  Member "particle filtering for large dimensional state spaces with multimodal observation likelihoods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review fight sample degeneracy and impoverishment in particle filters a review of intelligent approaches")

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)