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 "supervised learning under distributed features"):
  Abstract: This paper studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in a network, where each agent observes some of the features. Through local cooperation, the agents are supposed to interact with each other to solve an inference problem and converge towards the global minimizer of an empirical risk. We study this problem exclusively in the primal domain, and propose new and effective distributed solutions with guaranteed convergence to the minimizer with linear rate under strong convexity. This is achieved by combining a dynamic diffusion construction, a pipeline strategy, and variance-reduced techniques. Simulation results illustrate the conclusions.

Includes members:
  Member "coordinate descent diffusion learning by networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic average diffusion with randomized coordinate updates")
  Member "variance reduced stochastic learning by networked agents under random reshuffling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "variance reduced stochastic learning under random reshuffling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "distributed learning for stochastic generalized nash equilibrium problems":
    also connected through other hyperedges:
      (none)
  Member "sdca without duality regularization and individual convexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions")
  Member "doubly random parallel stochastic methods for large scale learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed big data optimization via block communications")
  Member "next in network nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "dynamic average diffusion with randomized coordinate updates")

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.MA(Multiagent Systems)