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 "on the asymptotic bias of the diffusion based distributed pareto optimization"):
  Abstract: We revisit the asymptotic bias analysis of the distributed Pareto optimization algorithm developed based on the diffusion strategies. We propose an alternative way to analyze the asymptotic bias of this algorithm at small step-sizes and show that the asymptotic bias descends to zero with a linear dependence on the largest step-size parameter when this parameter is sufficiently small. In addition, through the proposed analytic approach, we provide an expression for the small-step-size asymptotic bias when a condition assumed jointly on the combination matrices and the step-sizes does not strictly hold. This is a likely scenario in practice, which has not been considered in the original paper that introduced the algorithm. Our methodology provides new insights into the inner workings of the diffusion Pareto optimization algorithm while being considerably less involved than the small-step-size asymptotic bias analysis presented in the original work. This is because we take advantage of the special eigenstructure of the composite combination matrix used in the algorithm without calling for any eigenspace decomposition or matrix inversion.

Includes members:
  Member "dictionary learning over distributed models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "distributed interference cancellation in multi agent scenarios")
  Member "information exchange and learning dynamics over weakly connected adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning graph influence from social interactions")
  Member "compressive diffusion strategies over distributed networks for reduced communication load":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "distributed diffusion based lms for node specific adaptive parameter estimation":
    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 "analysis of a reduced communication diffusion lms algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diffusion leaky lms algorithm analysis and implementation")
  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.DC(Distributed, Parallel, and Cluster Computing)