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 gradient methods with variable number of working nodes"):
  Abstract: We consider distributed optimization where   $N$   nodes in a connected network minimize the sum of their local costs subject to a common constraint set. We propose a distributed projected gradient method where each node, at each iteration    $k$  , performs an update (is active) with probability    ${p}_{k}$  , and stays idle (is inactive) with probability   $1-{p}_{k}$  . Whenever active, each node performs an update by weight-averaging its solution estimate with the estimates of its active neighbors, taking a negative gradient step with respect to its local cost, and performing a projection onto the constraint set; inactive nodes perform no updates. Assuming that nodes' local costs are strongly convex, with Lipschitz continuous gradients, we show that, as long as activation probability   ${p}_{k}$    grows to one asymptotically, our algorithm converges in the mean square sense (MSS) to the same solution as the standard distributed gradient method, i.e., as if all the nodes were active at all iterations. Moreover, when   ${p}_{k}$   grows to one linearly, with an appropriately set convergence factor, the algorithm has a linear MSS convergence, with practically the same factor as the standard distributed gradient method. Simulations on both synthetic and real world data sets demonstrate that, when compared with the standard distributed gradient method, the proposed algorithm significantly reduces the overall number of per-node communications and per-node gradient evaluations (computational cost) for the same required accuracy.

Includes members:
  Member "hybrid random deterministic parallel algorithms for nonconvex big data optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed partitioned big data optimization via asynchronous dual decomposition")
  Member "on the influence of informed agents on learning and adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "asynchronous adaptation and learning over networks part iii comparison analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coordinate descent diffusion learning by networked agents")
  Member "asynchronous adaptation and learning over networks part ii performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coordinate descent diffusion learning by networked agents")
  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 "hybrid deterministic stochastic methods for data fitting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "a fast distributed proximal gradient method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed big data optimization via block communications")
  Member "fast distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on local computation for optimization in multi agent systems")

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)