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 "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.

Includes members:
  Member "a polyhedral approximation framework for convex and robust distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a duality based approach for distributed optimization with coupling constraints")
  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 "convergence rates of distributed nesterov like gradient methods on random networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed nesterov gradient methods over arbitrary graphs")
  Member "explicit convergence rate of a distributed alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "networked parallel algorithms for robust convex optimization via the scenario approach")
  Member "a fast distributed proximal gradient method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed big data optimization via block communications")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised learning under distributed features")
  Member "distributed abstract optimization via constraints consensus theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "social power evolution in influence networks with stubborn individuals")

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)