human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a proximal dual consensus admm method for multi agent constrained optimization"):
  Abstract: This paper considers a convex optimization problem with a globally coupled linear equality constraint and local polyhedron constraints and develops efficient distributed optimization methods. The considered problem has many engineering applications. Due to the polyhedron constraints, agents in the existing methods have to deal with polyhedron constrained subproblems at each iteration. One of the key challenges is that projection onto a polyhedron set is not trivial, which prohibits the agents from solving these subproblems efficiently. In this paper, based on the alternating direction method of multipliers (ADMM), we propose a new distributed optimization method, called proximal dual consensus ADMM (PDC-ADMM). The PDC-ADMM transforms the polyhedron constraints as quadratic penalty terms in the subproblems, making the subproblems efficiently solvable and consequently reducing the overall computational overhead of the agents. In addition, we propose a randomized PDC-ADMM which can deal with time-varying networks with randomly  on/off  agents and communication errors, and an inexact (randomized) PDC-ADMM for low-complexity computations. We show that the proposed distributed methods converge to the optimal solution set almost surely and have a   $\mathcal{O}\left(1/k\right)$   ergodic convergence rate in the mean. Numerical results show that the proposed methods offer significantly lower computation time than the existing distributed ADMM method in solving a linearly constrained LASSO problem.

Includes members:
  Member "multi agent distributed optimization via inexact consensus admm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "parallel selective algorithms for big data optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a randomized primal distributed algorithm for partitioned and big data non convex optimization")
  Member "distributed constrained optimization by consensus based primal dual perturbation method":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures")
  Member "asynchronous distributed optimization using a randomized alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "popsgd decentralized stochastic gradient descent in the population model")
  Member "coordinated home energy management for real time power balancing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed constrained optimization by consensus based primal dual perturbation method")
  Member "distributed robust multicell coordinated beamforming with imperfect csi an admm approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "cloud computing and grid computing 360 degree compared":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anchoring the value of cryptocurrency")

Question: Please tell me which class the source paper 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)