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: "sequential randomized algorithms for convex optimization in the presence of uncertainty"
  Abstract: In this technical note, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respectively, is given. The proposed methods allow to enlarge the applicability of the existing randomized methods to real-world applications involving a large number of design variables. Since the proposed approach does not provide  a priori  bounds on the sample complexity, extensive numerical simulations, dealing with an application to hard-disk drive servo design, are provided. These simulations testify the goodness of the proposed solution.

Connected through hyperedge HE-1 (induced by source paper "optimization under uncertainty in the era of big data and deep learning when machine learning meets mathematical programming"):
  with members:
    - "model free renewable scenario generation using generative adversarial networks"
    - "data driven stochastic robust optimization general computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a scenario approach for non convex control design"
    - "data driven robust taxi dispatch under demand uncertainties"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "robust linear static anti windup with probabilistic certificates"):
  with members:
    - "scenario optimization with certificates and applications to anti windup design"

Connected through hyperedge HE-3 (induced by source paper "scenario optimization with certificates and applications to anti windup design"):
  with members:
    - "randomized methods for design of uncertain systems sample complexity and sequential algorithms"

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)