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 representations for packetized predictive networked control"
  Abstract: We investigate a networked control architecture for LTI plant models with a scalar input. Communication from controller to actuator is over an unreliable network which introduces packet dropouts. To achieve robustness against dropouts, we adopt a packetized predictive control paradigm wherein each control packet transmitted contains tentative future plant input values. The novelty of our approach is that we seek that the control packets transmitted be sparse. For that purpose, we adapt tools from the area of compressed sensing and propose to design the control packets via on-line minimization of a suitable L1/L2 cost function. We then show how to choose parameters of the cost function to ensure that the resultant closed loop system be practically stable, provided the maximum number of consecutive packet dropouts is bounded. A numerical example illustrates that sparsity reduces bit-rates, thereby making our proposal suited to control over unreliable and bit-rate limited networks.

Connected through hyperedge HE-1 (induced by source paper "optimal control of linear systems with limited control actions threshold based event triggered control"):
  with members:
    - "multiple loop self triggered model predictive control for network scheduling and control"
    - "sparse packetized predictive control for networked control over erasure channels"
    - "an improved three weight message passing algorithm"

Connected through hyperedge HE-2 (induced by source paper "maximum hands off control a paradigm of control effort minimization"):
  with members:
    - "sparse packetized predictive control for networked control over erasure channels"

Connected through hyperedge HE-3 (induced by source paper "sparse packetized predictive control for networked control over erasure channels"):
  with members:
    - "compressive sampling for networked feedback control"
    - "compressive sampling for remote control systems"
    - "packetized predictive control for rate limited networks via sparse representation"
    - "sparse command generator for remote control"

Connected through hyperedge HE-4 (induced by source paper "sparsity methods for networked control"):
  with members:
    - "sparse packetized predictive control for networked control over erasure channels"

Connected through hyperedge HE-5 (induced by source paper "hands off control as green control"):
  with members:
    - "sparse packetized predictive control for networked control over erasure channels"

Connected through hyperedge HE-6 (induced by source paper "l1 optimal splines for outlier rejection"):
  with members:
    - "monotone smoothing splines using general linear systems"
    - "compressive sampling for remote control systems"
    - "packetized predictive control for rate limited networks via sparse representation"

Connected through hyperedge HE-7 (induced by source paper "compressive sampling for networked feedback control"):
  with members:
    - "compressive sampling for remote control systems"
    - "sparse command generator for remote control"

Connected through hyperedge HE-8 (induced by source paper "compressive sampling for remote control systems"):
  with members:
    - "sparse command generator for remote 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)