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: "embedded online optimization for model predictive control at megahertz rates"
  Abstract: Faster, cheaper, and more power efficient optimization solvers than those currently possible using general-purpose techniques are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.

Connected through hyperedge HE-1 (induced by source paper "large scale model predictive control with neural networks and primal active sets"):
  with members:
    - "safe and near optimal policy learning for model predictive control using primal dual neural networks"
    - "learning an approximate model predictive controller with guarantees"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "understanding deep neural networks with rectified linear units"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "the blas api of blasfeo optimizing performance for small matrices"):
  with members:
    - "a class of parallel tiled linear algebra algorithms for multicore architectures"

Connected through hyperedge HE-3 (induced by source paper "automatic software and computing hardware codesign for predictive control"):
  with members:
    - "nonlinear predictive control on a heterogeneous computing platform"

Connected through hyperedge HE-4 (induced by source paper "energy efficient real time scheduling for two type heterogeneous multiprocessors"):
  with members:
    - "feedback scheduling for energy efficient real time homogeneous multiprocessor systems"

Connected through hyperedge HE-5 (induced by source paper "feedback and time are essential for the optimal control of computing systems"):
  with members:
    - "a convex feasibility approach to anytime model predictive 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)