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: "pafimocs particle filtered modified cs and applications in visual tracking across illumination change"
  Abstract: We study the problem of tracking (causally estimating) a time sequence of sparse spatial signals with changing sparsity patterns, as well as other unknown states, from a sequence of nonlinear observations corrupted by (possibly) non-Gaussian noise. In many applications, particularly those in visual tracking, the unknown state can be split into a small dimensional part, e.g. global motion, and a spatial signal, e.g. illumination or shape deformation. The spatial signal is often well modeled as being sparse in some domain. For a long sequence, its sparsity pattern can change over time, although the changes are usually slow. To address the above problem, we propose a novel solution approach called Particle Filtered Modified-CS (PaFiMoCS). The key idea of PaFiMoCS is to importance sample for the small dimensional state vector, while replacing importance sampling by slow sparsity pattern change constrained posterior mode tracking for recovering the sparse spatial signal. We show that the problem of tracking moving objects across spatially varying illumination change is an example of the above problem and explain how to design PaFiMoCS for it. Experiments on both simulated data as well as on real videos with significant illumination changes demonstrate the superiority of the proposed algorithm as compared with existing particle filter based tracking algorithms.

Connected through hyperedge HE-1 (induced by source paper "nonlinear compressive particle filtering"):
  with members:
    - "quadratic basis pursuit"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "compressed sensing with nonlinear observations and related nonlinear optimisation problems"
    - "kalman filtered compressed sensing"
    - "particle filtering for large dimensional state spaces with multimodal observation likelihoods"

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.CV(Computer Vision and Pattern Recognition)