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: "control interpretations for first order optimization methods"
  Abstract: First-order iterative optimization methods play a fundamental role in large scale optimization and machine learning. This paper presents control interpretations for such optimization methods. First, we give loop-shaping interpretations for several existing optimization methods and show that they are composed of basic control elements such as PID and lag compensators. Next, we apply the small gain theorem to draw a connection between the convergence rate analysis of optimization methods and the input-output gain computations of certain complementary sensitivity functions. These connections suggest that standard classical control synthesis tools may be brought to bear on the design of optimization algorithms.

Connected through hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-3 (induced by source paper "direct synthesis of iterative algorithms with bounds on achievable worst case convergence rate"):
  with members:
    - "exponential stability analysis via integral quadratic constraints"

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)