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 adaptation over multi agent networks with wireless link impairments"):
  Abstract: We study the performance of diffusion least-mean squares algorithms for distributed parameter estimation in multi-agent networks when nodes exchange information over wireless communication links. Wireless channel impairments, such as fading and path-loss, adversely affect the exchanged data and cause instability and performance degradation if left unattended. To mitigate these effects, we incorporate equalization coefficients into the diffusion combination step and update the combination weights dynamically in the face of randomly changing neighborhoods due to fading conditions. When channel state information (CSI) is unavailable, we determine the equalization factors from pilot-aided channel coefficient estimates. The analysis reveals that by properly monitoring the CSI over the network and choosing sufficiently small adaptation step-sizes, the diffusion strategies are able to deliver satisfactory performance in the presence of fading and path loss.

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 "estimation of space time varying parameters using a diffusion lms algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supervised learning under distributed features")
  Member "weight optimization for consensus algorithms with correlated switching topology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online resource inference in network utility maximization problems")
  Member "distributing the kalman filter for large scale systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coded distributed tracking")
  Member "distributed consensus algorithms in sensor networks link failures and channel noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multitask diffusion adaptation over networks with common latent representations")

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)