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 "proximal multitask learning over networks with sparsity inducing coregularization"):
  Abstract: In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entries. We propose a fully distributed algorithm for solving this problem. The approach relies on minimizing a global mean-square error criterion regularized by nondifferentiable terms to promote cooperation among neighboring clusters. A general diffusion forward–backward splitting strategy is introduced. Then, it is specialized to the case of sparsity promoting regularizers. A closed-form expression for the proximal operator of a weighted sum of   $\ell _1$  -norms is derived to achieve higher efficiency. We also provide conditions on the step-sizes that ensure convergence of the algorithm in the mean and mean-square error sense. Simulations are conducted to illustrate the effectiveness of the strategy.

Includes members:
  Member "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "diffusion adaptation strategies for distributed estimation over gaussian markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive diffusion schemes for heterogeneous networks")
  Member "multitask diffusion adaptation over asynchronous networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "diffusion lms for clustered multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "multitask diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")

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.SY(Systems and Control)