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 packetized predictive control for networked control over erasure channels"
  Abstract: We study feedback control over erasure channels with packet-dropouts. To achieve robustness with respect to packet-dropouts, the controller transmits data packets containing plant input predictions, which minimize a finite horizon cost function. To reduce the data size of packets, we propose to adopt sparsity-promoting optimizations, namely,   $ \ell ^{1} {\rm \mathchar"702D }\ell ^{2}$   and    $\ell ^{2}$  -constrained    $\ell ^{0}$   optimizations, for which efficient algorithms exist. We show how to design the tuning parameters to ensure (practical) stability of the resulting feedback control systems when the number of consecutive packet-dropouts is bounded.

Connected through hyperedge HE-1 (induced by source paper "sparsefool a few pixels make a big difference"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "spatially transformed adversarial examples"
    - "attacking convolutional neural network using differential evolution"
    - "audio adversarial examples targeted attacks on speech to text"
    - "with friends like these who needs adversaries"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "geometric robustness of deep networks analysis and improvement"

Connected through hyperedge HE-2 (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"
    - "an improved three weight message passing algorithm"
    - "sparse representations for packetized predictive networked control"

Connected through hyperedge HE-3 (induced by source paper "continuous hands off control by clot norm minimization"):
  with members:
    - "maximum hands off control a paradigm of control effort minimization"
    - "characterization of maximum hands off control"

Connected through hyperedge HE-4 (induced by source paper "maximum hands off control a paradigm of control effort minimization"):
  with members:
    - "sparse representations for packetized predictive networked control"

Connected through hyperedge HE-5 (induced by source paper "characterization of maximum hands off control"):
  with members:
    - "maximum hands off control a paradigm of control effort minimization"

Connected through hyperedge HE-6 (induced by source paper "l 1 control theoretic smoothing splines"):
  with members:
    - "monotone smoothing splines using general linear systems"
    - "l1 optimal splines for outlier rejection"

Connected through hyperedge HE-7 (induced by source paper "sparsity methods for networked control"):
  with members:
    - "sparse representations for packetized predictive networked control"

Connected through hyperedge HE-8 (induced by source paper "hands off control as green control"):
  with members:
    - "sparse representations for packetized predictive networked 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)