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 "scale free loopy structure is resistant to noise in consensus dynamics in complex networks"):
  Abstract: The vast majority of real-world networks are scale-free, loopy, and sparse, with a power-law degree distribution and a constant average degree. In this paper, we study first-order consensus dynamics in binary scale-free networks, where vertices are subject to white noise. We focus on the coherence of networks characterized in terms of the $H_2$-norm, which quantifies how closely agents track the consensus value. We first provide a lower bound of coherence of a network in terms of its average degree, which is independent of the network order. We then study the coherence of some sparse, scale-free real-world networks, which approaches a constant. We also study numerically the coherence of Barab\'asi-Albert networks and high-dimensional random Apollonian networks, which also converges to a constant when the networks grow. Finally, based on the connection of coherence and the Kirchhoff index, we study analytically the coherence of two deterministically-growing sparse networks and obtain the exact expressions, which tend to small constants. Our results indicate that the effect of noise on the consensus dynamics in power-law networks is negligible. We argue that scale-free topology, together with loopy structure, is responsible for the strong robustness with respect to noisy consensus dynamics in power-law networks.

Includes members:
  Member "maximum matchings and minimum dominating sets in apollonian networks and extended tower of hanoi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "edge corona product as an approach to modeling complex simplical networks")
  Member "domination number and minimum dominating sets in pseudofractal scale free web and sierpinski graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "independence number and the number of maximum independent sets in pseudofractal scale free web and sierpi nski gasket")
  Member "control principles of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graph learning over optimization networks")
  Member "consensus and coherence in fractal networks":
    also connected through other hyperedges:
      (none)
  Member "network based consensus averaging with general noisy channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotic properties of distributed social sampling algorithm")

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)