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: "n2sid nuclear norm subsystem identification"
  Abstract: The data equation in subspace identification relates Hankel matrices constructed from input and output data. The matrices in this equation, which are derived from the system matrices of the unknown system, contain important structural information. Such information is the low rank property of the product between the extended observability matrix and the state sequence, and the Toeplitz structure of the matrix of Markov parameters of the system in innovation form. We will show that nuclear norm subspace identification makes it possible to formulate these structural properties as constraints in a single convex multi-criteria optimization problem. This problem seeks a trade-off between a nuclear norm minimization problem to retrieve the subspace of interest and a prediction error criterium to find an optimal match between the measured and predicted output. The advantage of our method over existing unconstrained and nuclear norm constrained subspace identification methods is that the structural constraints are in general improving the estimates when dealing with short data sets, i.e. where the number of measurements is a small multiple of the system order. This advantage is demonstrated in a validation study making use of data sets from the DaSIy library.

Connected through hyperedge HE-1 (induced by source paper "regularization and bayesian learning in dynamical systems past present and future"):
  with members:
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "approximate regularization paths for nuclear norm minimization using singular value bounds with implementation and extended appendix"
    - "regularized system identification using orthonormal basis functions"
    - "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"
    - "relevance singular vector machine for low rank matrix sensing"
    - "kernels for linear time invariant system identification"

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)