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 "convergence rates of distributed nesterov like gradient methods on random networks"):
  Abstract: We consider distributed optimization in random networks where N nodes cooperatively minimize the sum \sum_{i=1}^N f_i(x) of their individual convex costs. Existing literature proposes distributed gradient-like methods that are computationally cheap and resilient to link failures, but have slow convergence rates. In this paper, we propose accelerated distributed gradient methods that: 1) are resilient to link failures; 2) computationally cheap; and 3) improve convergence rates over other gradient methods. We model the network by a sequence of independent, identically distributed random matrices {W(k)} drawn from the set of symmetric, stochastic matrices with positive diagonals. The network is connected on average and the cost functions are convex, differentiable, with Lipschitz continuous and bounded gradients. We design two distributed Nesterov-like gradient methods that modify the D-NG and D-NC methods that we proposed for static networks. We prove their convergence rates in terms of the expected optimality gap at the cost function. Let k and K be the number of per-node gradient evaluations and per-node communications, respectively. Then the modified D-NG achieves rates O(log k/k) and O(\log K/K), and the modified D-NC rates O(1/k^2) and O(1/K^{2-\xi}), where \xi>0 is arbitrarily small. For comparison, the standard distributed gradient method cannot do better than \Omega(1/k^{2/3}) and \Omega(1/K^{2/3}), on the same class of cost functions (even for static networks). Simulation examples illustrate our analytical findings.

Includes members:
  Member "asynchronous distributed optimization using a randomized alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "popsgd decentralized stochastic gradient descent in the population model")
  Member "a fast distributed proximal gradient method":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed big data optimization via block communications")
  Member "a sparsity promoting adaptive algorithm for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coordinate descent diffusion learning by networked agents")
  Member "fast distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on local computation for optimization in multi agent systems")
  Member "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inverse graph learning over optimization networks")
  Member "weight optimization for consensus algorithms with correlated switching topology":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online resource inference in network utility maximization problems")
  Member "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond")

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)