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 over multitask networks"):
  Abstract: The diffusion LMS algorithm has been extensively studied in recent years. This efficient strategy allows to address distributed optimization problems over networks in the case where nodes have to collaboratively estimate a single parameter vector. Nevertheless, there are several problems in practice that are multitask-oriented in the sense that the optimum parameter vector may not be the same for every node. This brings up the issue of studying the performance of the diffusion LMS algorithm when it is run, either intentionally or unintentionally, in a multitask environment. In this paper, we conduct a theoretical analysis on the stochastic behavior of diffusion LMS in the case where the single-task hypothesis is violated. We analyze the competing factors that influence the performance of diffusion LMS in the multitask environment, and which allow the algorithm to continue to deliver performance superior to non-cooperative strategies in some useful circumstances. We also propose an unsupervised clustering strategy that allows each node to select, via adaptive adjustments of combination weights, the neighboring nodes with which it can collaborate to estimate a common parameter vector. Simulations are presented to illustrate the theoretical results, and to demonstrate the efficiency of the proposed clustering strategy.

Includes members:
  Member "estimation of space time varying parameters using a diffusion lms algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "distributed diffusion based lms for node specific parameter estimation over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multitask diffusion adaptation over networks with common latent representations")
  Member "multitask diffusion adaptation over networks":
    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 "distributed pareto optimization via diffusion strategies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "supervised learning under distributed features")
  Member "sparse distributed learning based on diffusion adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery")
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data")
  Member "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")
  Member "a sparsity promoting adaptive algorithm for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "coordinate descent diffusion learning by networked agents")

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)