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 "diffusion lms for multitask problems with local linear equality constraints"):
  Abstract: We consider distributed multitask learning problems over a network of agents where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equality constraints. Each agent possesses its own convex cost function of its parameter vector and a set of linear equality constraints involving its own parameter vector and the parameter vectors of its neighboring agents. We propose an adaptive stochastic algorithm based on the projection gradient method and diffusion strategies in order to allow the network to optimize the individual costs subject to all constraints. Although the derivation is carried out for linear equality constraints, the technique can be applied to other forms of convex constraints. We conduct a detailed mean-square-error analysis of the proposed algorithm and derive closed-form expressions to predict its learning behavior. We provide simulations to illustrate the theoretical findings. Finally, the algorithm is employed for solving two problems in a distributed manner: a minimum-cost flow problem over a network and a space-time varying field reconstruction problem.

Includes members:
  Member "proximal multitask learning over networks with sparsity inducing coregularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed learning in non convex environments part ii polynomial escape from saddle points")
  Member "dictionary learning over distributed models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised learning under distributed features")
  Member "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "diffusion lms over multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed interference cancellation in multi agent scenarios")
  Member "distributed diffusion based lms for node specific adaptive parameter estimation":
    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 "multitask diffusion adaptation over asynchronous networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "estimation of space time varying parameters using a diffusion lms algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "on the mean square performance of the constrained lms algorithm":
    also connected through other hyperedges:
      (none)

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)