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 "dynamic average diffusion with randomized coordinate updates"):
  Abstract: This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observation vector, and different agents may select different entries of their observations before engaging in a consultation step. Careful coordination of the interactions among agents is necessary to avoid bias and ensure convergence. We provide a convergence analysis for the proposed methods, and illustrate the results by means of simulations.

Includes members:
  Member "supervised learning under distributed features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "stochastic gradient push for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "coordinate descent diffusion learning by networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supervised learning under distributed features")
  Member "distributed big data optimization via block iterative convexification and averaging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed big data optimization via block communications")
  Member "next in network nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "dictionary learning over distributed models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised learning under distributed features")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "distributed pareto optimization via diffusion strategies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "supervised learning under distributed features")

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.SI(Social and Information Networks)