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: "sparse command generator for remote control"
  Abstract: In this article, we consider remote-controlled systems, where the command generator and the controlled object are connected with a bandwidth-limited communication link. In the remote-controlled systems, efficient representation of control commands is one of the crucial issues because of the bandwidth limitations of the link. We propose a new representation method for control commands based on compressed sensing. In the proposed method, compressed sensing reduces the number of bits in each control signal by representing it as a sparse vector. The compressed sensing problem is solved by an l1−l2 optimization, which can be effectively implemented with an iterative shrinkage algorithm. A design example also shows the effectiveness of the proposed method.

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"
    - "compressive sampling for remote control systems"
    - "packetized predictive control for rate limited networks via sparse representation"

Connected through hyperedge HE-2 (induced by source paper "compressive sampling for networked feedback control"):
  with members:
    - "sparse representations for packetized predictive networked control"
    - "compressive sampling for remote control systems"

Connected through hyperedge HE-3 (induced by source paper "compressive sampling for remote control systems"):
  with members:
    - "sparse representations for packetized predictive networked control"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

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)