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: "observability with random observations"
  Abstract: Recovering or estimating the initial state of a high-dimensional system can require a large number of measurements. In this paper, we explain how this burden can be significantly reduced for certain linear systems when randomized measurement operators are employed. Our work builds upon recent results from Compressive Sensing (CS) and sparse signal recovery. In particular, we make the connection to CS analysis for random block diagonal matrices. #R##N#By deriving Concentration of Measure (CoM) inequalities, we show that the observability matrix satisfies the Restricted Isometry Property (RIP) (a sufficient condition on the measurement matrix for stable recovery of sparse vectors) under certain conditions on the state transition matrix. For example, we show that if the state transition matrix is unitary, and if independent, randomly-populated measurement matrices are employed, then it is possible to uniquely recover a sparse high-dimensional initial state when the total number of measurements scales linearly in the sparsity level (the number of non-zero entries) of the initial state and logarithmically in the state dimension. This is in fact a significant reduction in the sufficient total number of measurement for correct initial state recovery as compared to traditional observability theory. We further extend our RIP analysis for scaled unitary and symmetric state transition matrices. We support our analysis with a case study of a two-dimensional diffusion process.

Connected through hyperedge HE-1 (induced by source paper "data driven sparse system identification"):
  with members:
    - "localized lqr optimal control"
    - "agnostic system identification for model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "optimal localization of diffusion sources in complex networks"):
  with members:
    - "information source detection in the sir model a sample path based approach"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-3 (induced by source paper "controllability gramian spectra of random networks"):
  with members:
    - "controllability and fraction of leaders in infinite networks"
    - "structural controllability of multi agent networks robustness against simultaneous failures"
    - "controllability metrics limitations and algorithms for complex networks"
    - "spectra of random graphs with arbitrary expected degrees"

Connected through hyperedge HE-4 (induced by source paper "compressive origin destination matrix estimation"):
  with members:
    - "reconstruction of arbitrary biochemical reaction networks a compressive sensing approach"

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)