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 "efficient distributed online prediction and stochastic optimization with approximate distributed averaging"):
  Abstract: We study distributed methods for online prediction and stochastic optimization. Our approach is iterative: in each round nodes first perform local computations and then communicate in order to aggregate information and synchronize their decision variables. Synchronization is accomplished through the use of a distributed averaging protocol. When an exact distributed averaging protocol is used, it is known that the optimal regret bound of $\mathcal{O}(\sqrt{m})$ can be achieved using the distributed mini-batch algorithm of Dekel et al. (2012), where $m$ is the total number of samples processed across the network. We focus on methods using approximate distributed averaging protocols and show that the optimal regret bound can also be achieved in this setting. In particular, we propose a gossip-based optimization method which achieves the optimal regret bound. The amount of communication required depends on the network topology through the second largest eigenvalue of the transition matrix of a random walk on the network. In the setting of stochastic optimization, the proposed gossip-based approach achieves nearly-linear scaling: the optimization error is guaranteed to be no more than $\epsilon$ after $\mathcal{O}(\frac{1}{n \epsilon^2})$ rounds, each of which involves $\mathcal{O}(\log n)$ gossip iterations, when nodes communicate over a well-connected graph. This scaling law is also observed in numerical experiments on a cluster.

Includes members:
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "communication efficient distributed dual coordinate ascent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic federated learning")
  Member "communication computation tradeoffs in consensus based distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on distributed machine learning")
  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 "distributed autonomous online learning regrets and intrinsic privacy preserving properties":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs")
  Member "optimal distributed online prediction using mini batches":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization networks")
  Member "optimization and analysis of distributed averaging with short node memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed asynchronous algorithms for solving positive definite linear equations over dynamic 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)