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 "a class of diffusion algorithms with logarithmic cost over adaptive sparse volterra network"):
  Abstract: In this Letter, we present a novel class of diffusion algorithms that can be used to estimate the coefficients of sparse Volterra network (SVN). The development of the algorithms is based on the logarithmic cost and l0-norm constraint. Simulations for Gaussian and impulsive scenarios are conducted to demonstrate the superior performance of the proposed algorithms as compared with the existing algorithms.

Includes members:
  Member "diffusion lms strategies in sensor networks with noisy input data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed interference cancellation in multi agent scenarios")
  Member "distributed decision making over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diffusion multi rate lms algorithm for acoustic sensor networks")
  Member "a novel family of adaptive filtering algorithms based on the logarithmic cost":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proportionate adaptive filtering under correntropy criterion in impulsive noise environments")
  Member "estimation of space time varying parameters using a diffusion lms algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "study of robust distributed diffusion rls algorithms with side information for adaptive networks")
  Member "performance limits for distributed estimation over lms adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detection under one bit messaging over adaptive networks")

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)