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: "a polyhedral approximation framework for convex and robust distributed optimization"
  Abstract: In this paper, we consider a general problem setup for a wide class of convex and robust distributed optimization problems in peer-to-peer networks. In this setup, convex constraint sets are distributed to the network processors who have to compute the optimizer of a linear cost function subject to the constraints. We propose a novel fully distributed and asynchronous algorithm, named cutting-plane consensus, to solve the problem, based on a polyhedral outer approximation of the constraint sets. Processors running the algorithm compute and exchange linear approximations of their locally feasible sets. Independently of the number of processors in the network, each processor stores only a small number of linear constraints, making the algorithm scalable to large networks. The cutting-plane consensus algorithm is presented and analyzed for the general framework. Specifically, we prove the correctness of the algorithm, and show its robustness against communication or processor failures. Then, the cutting-plane consensus algorithm is specified to three different classes of distributed optimization problems, namely 1) inequality constrained problems, 2) robust optimization problems, and 3) almost separable optimization problems. For each one of these problem classes we solve a concrete problem and present computational results. That is, we show how to solve: position estimation in wireless sensor networks, a distributed robust linear program, and a distributed microgrid control problem.

Connected through hyperedge HE-1 (induced by source paper "a duality based approach for distributed optimization with coupling constraints"):
  with members:
    - "a duality based approach for distributed min max optimization with application to demand side management"
    - "a proximal dual consensus admm method for multi agent constrained optimization"
    - "distributed constrained optimization by consensus based primal dual perturbation method"

Connected through hyperedge HE-2 (induced by source paper "distributed partitioned big data optimization via asynchronous dual decomposition"):
  with members:
    - "asynchronous distributed optimization via randomized dual proximal gradient"
    - "multi agent distributed optimization via inexact consensus admm"
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"

Connected through hyperedge HE-3 (induced by source paper "constraint coupled distributed optimization a relaxation and duality approach"):
  with members:
    - "a duality based approach for distributed optimization with coupling constraints"
    - "next in network nonconvex optimization"
    - "asynchronous distributed optimization via randomized dual proximal gradient"
    - "multi agent distributed optimization via inexact consensus admm"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "fast distributed gradient methods"

Connected through hyperedge HE-4 (induced by source paper "a duality based approach for distributed min max optimization with application to demand side management"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "a regularized saddle point algorithm for networked optimization with resource allocation constraints"
    - "distributed abstract optimization via constraints consensus theory and applications"

Connected through hyperedge HE-5 (induced by source paper "asynchronous distributed optimization via randomized dual proximal gradient"):
  with members:
    - "parallel selective algorithms for big data optimization"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "a fast distributed proximal gradient method"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed abstract optimization via constraints consensus theory and applications"

Connected through hyperedge HE-6 (induced by source paper "on the stability and convergence of a class of consensus systems with a nonlinear input"):
  with members:
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"

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)