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: "compressive sampling for remote control systems"
  Abstract: In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse representation is formulated by cardinality-constrained l2 optimization of the control performance, which is reducible to l1-l2 optimization. The low rate random sampling employed in the proposed method based on the compressive sampling, in addition to the fact that the l1-l2 optimization can be effectively solved by a fast iteration method, enables us to generate the sparse control signal with reduced computational complexity, which is preferable in remote control systems where computation delays seriously degrade the performance. We give a theoretical result for control performance analysis based on the notion of restricted isometry property (RIP). An example is shown to illustrate the effectiveness of the proposed approach via numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "sparse packetized predictive control for networked control over erasure channels"):
  with members:
    - "sparse representations for packetized predictive networked control"
    - "compressive sampling for networked feedback control"
    - "packetized predictive control for rate limited networks via sparse representation"
    - "sparse command generator for remote control"

Connected through hyperedge HE-2 (induced by source paper "min max piecewise constant optimal control for multi model linear systems"):
  with members:
    - "compressive sampling for networked feedback control"

Connected through hyperedge HE-3 (induced by source paper "networked estimation using sparsifying basis prediction"):
  with members:
    - "compressive sampling for networked feedback control"
    - "technical report observability of a linear system under sparsity constraints"
    - "packetized predictive control for rate limited networks via sparse representation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "l1 optimal splines for outlier rejection"):
  with members:
    - "monotone smoothing splines using general linear systems"
    - "sparse representations for packetized predictive networked control"
    - "packetized predictive control for rate limited networks via sparse representation"

Connected through hyperedge HE-5 (induced by source paper "compressive sampling for networked feedback control"):
  with members:
    - "sparse representations for packetized predictive networked control"
    - "sparse command generator for remote control"

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)