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: "asynchronous distributed optimization via randomized dual proximal gradient"
  Abstract: In this paper we consider distributed optimization problems in which the cost function is separable, i.e., a sum of possibly non-smooth functions all sharing a common variable, and can be split into a strongly convex term and a convex one. The second term is typically used to encode constraints or to regularize the solution. We propose a class of distributed optimization algorithms based on proximal gradient methods applied to the dual problem. We show that, by choosing suitable primal variable copies, the dual problem is itself separable when written in terms of conjugate functions, and the dual variables can be stacked into non-overlapping blocks associated to the computing nodes. We first show that a weighted proximal gradient on the dual function leads to a synchronous distributed algorithm with local dual proximal gradient updates at each node. Then, as main paper contribution, we develop asynchronous versions of the algorithm in which the node updates are triggered by local timers without any global iteration counter. The algorithms are shown to be proper randomized block-coordinate proximal gradient updates on the dual function.

Connected through hyperedge HE-1 (induced by source paper "asyspa an exact asynchronous algorithm for convex optimization over digraphs"):
  with members:
    - "distributed discrete time optimization in multiagent networks using only sign of relative state"
    - "asynchronous decentralized optimization in directed networks"
    - "asynchronous subgradient push"
    - "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "average consensus on general strongly connected digraphs"

Connected through hyperedge HE-2 (induced by source paper "distributed partitioned big data optimization via asynchronous dual decomposition"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "a polyhedral approximation framework for convex and robust distributed optimization"
    - "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"
    - "multi agent distributed optimization via inexact consensus admm"
    - "a polyhedral approximation framework for convex and robust distributed optimization"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "fast distributed gradient methods"

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)