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 "adaptive diffusion schemes for heterogeneous networks"):
  Abstract: In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i.e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size. Although such heterogeneous networks have been considered from the first works on diffusion networks, obtaining practical and robust schemes to adaptively adjust the combiners in different scenarios is still an open problem. In this paper, we study a diffusion strategy specially designed and suited to heterogeneous networks. Our approach is based on two key ingredients: 1) the adaptation and combination phases are completely decoupled, so that network nodes keep purely local estimations at all times and 2) combiners are adapted to minimize estimates of the network mean-square-error. Our scheme is compared with the standard adapt-then-combine scheme and theoretically analyzed using energy conservation arguments. Several experiments involving networks with heterogeneous nodes show that the proposed decoupled adapt-then-combine approach with adaptive combiners outperforms other state-of-the-art techniques, becoming a competitive approach in these scenarios.

Includes members:
  Member "diffusion lms over multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed interference cancellation in multi agent scenarios")
  Member "compressive diffusion strategies over distributed networks for reduced communication load":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-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 least mean squares estimation of graph signals")
  Member "on the influence of informed agents on learning and adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "sparse distributed learning based on diffusion adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery")
  Member "asynchronous adaptation and learning over networks part i modeling and stability analysis":
    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 learning behavior of adaptive networks part i transient analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inverse graph learning over optimization 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)