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 "distributed second order methods with variable number of working nodes"):
  Abstract: Recently, an idling mechanism has been introduced in the context of distributed first order methods for minimization of a sum of nodes' local convex costs over a generic, connected network. With the idling mechanism, each node i, at each iteration k, is active -- updates its solution estimate and exchanges messages with its network neighborhood -- with probability pk (pk increasing to one as k grows large), and it stays idle with probability 1-pk, while the activations are independent both across nodes and across iterations. The idling mechanism involves an increasing number of nodes in the algorithm (on average) as the iteration counter k grows, thus avoiding unnecessarily expensive exact updates at the initial iterations while performing beneficial close-to-exact updates near the solution. In this paper, we demonstrate that the idling mechanism can be successfully incorporated in distributed second order methods also. Specifically, we apply the idling mechanism to the recently proposed Distributed Quasi Newton method (DQN). We first show theoretically that DQN with idling exhibits very similar theoretical convergence and convergence rates properties as the standard DQN method, thus achieving the same order of convergence rate (R-linear) as the standard DQN, but with significantly cheaper updates. Further, we demonstrate by simulation examples significant communication and computational savings gained through incorporation of the idling mechanism.

Includes members:
  Member "asynchronous distributed admm for large scale optimization part ii linear convergence analysis and numerical performance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "harnessing the power of serverless runtimes for large scale optimization")
  Member "distributed gradient methods with variable number of working nodes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "newton like method with diagonal correction for distributed optimization")
  Member "newton like method with diagonal correction for distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block compressed sensing based distributed device detection for m2m communications")
  Member "hybrid deterministic stochastic methods for data fitting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convergence time optimization for federated learning over wireless networks")

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.IT(Information Theory)