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 "asyspa an exact asynchronous algorithm for convex optimization over digraphs"):
  Abstract: This paper proposes a novel exact asynchronous subgradient-push algorithm (AsySPA) to solve an additive cost optimization problem over digraphs where each node only has access to a local convex function and updates asynchronously with an arbitrary rate. Specifically, each node of a strongly connected digraph does not wait for updates from other nodes but simply starts a new update within any bounded time interval by using local information available from its in-neighbors. "Exact" means that every node of the AsySPA can asymptotically converge to the same optimal solution, even under different update rates among nodes and bounded communication delays. To compensate uneven update rates, we design a simple mechanism to adaptively adjust stepsizes per update in each node, which is substantially different from the existing works. Then, we construct a delay-free augmented system to address asynchrony and delays, and perform the convergence analysis by proposing a generalized subgradient algorithm, which clearly has its own significance and helps us to explicitly evaluate the convergence speed of the AsySPA. Finally, we demonstrate advantages of the AsySPA over the celebrated synchronous SPA in both theory and simulation.

Includes members:
  Member "distributed discrete time optimization in multiagent networks using only sign of relative state":
    also connected through other hyperedges:
      (none)
  Member "asynchronous decentralized optimization in directed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks")
  Member "asynchronous subgradient push":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks")
  Member "distributed convex optimization with inequality constraints over time varying unbalanced digraphs":
    also connected through other hyperedges:
      (none)
  Member "distributed nonconvex multiagent optimization over time varying networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clustering with distributed data")
  Member "asynchronous distributed optimization via randomized dual proximal gradient":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed partitioned big data optimization via asynchronous dual decomposition")
  Member "asynchronous adaptation and learning over networks part i modeling and stability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "average consensus on general strongly connected digraphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed nesterov gradient methods over arbitrary graphs")

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)