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 "networked parallel algorithms for robust convex optimization via the scenario approach"):
  Abstract: This chapter proposes a parallel computing framework to distributedly solve robust convex optimization (RCO) when the constraints are affected by nonlinear uncertainty. To this end, we adopt a scenario approach by randomly sampling the uncertainty set. However, the number of samples to attain high levels of probabilistic guarantee of robustness may be large, which results in a large number of constraints in the scenario problem (SP). Instead of using a single processor, we resort to many processors that are distributed among different nodes of time-varying unbalanced digraphs. Then, we propose a random projected algorithm solve the SP, which is given in an explicitly recursive form with simple iteration. We show that if the sequence of digraphs are uniformly jointly strongly connected (UJSC), each node asymptotically converges to a common optimal solution to the SP. That is, the RCO is effectively solved in a distributed parallel way.

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 "distributed constrained optimization by consensus based primal dual perturbation method":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures")
  Member "explicit convergence rate of a distributed alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed algorithms for computation of centrality measures in complex networks")
  Member "robust regression and lasso":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a fast saddle point dynamical system approach to robust deep learning")

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.DC(Distributed, Parallel, and Cluster Computing)