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 "sampled data consensus over random networks"):
  Abstract: This paper considers the consensus problem for a network of nodes with random interactions and sampled-data control actions. We first show that consensus in expectation, in mean square, and almost surely are equivalent for a general random network model when the inter-sampling interval and maximum node degree satisfy a simple relation. The three types of consensus are shown to be simultaneously achieved over an independent or a Markovian random network defined on an underlying graph with a directed spanning tree. For both independent and Markovian random network models, necessary and sufficient conditions for mean-square consensus are derived in terms of the spectral radius of the corresponding state transition matrix. These conditions are then interpreted as the existence of critical value on the intersampling interval, below which a global mean-square consensus is achieved and above which the system diverges in a mean-square sense for some initial states. Finally, we establish an upper bound on the intersampling interval below which almost sure consensus is reached, and a lower bound on the intersampling interval above which almost sure divergence is reached. Some numerical simulations are given to validate the theoretical results and some discussions on the critical value of the inter-sampling intervals for the mean-square consensus are provided.

Includes members:
  Member "opinion fluctuations and disagreement in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shifting opinions in a social network through leader selection")
  Member "how agreement and disagreement evolve over random dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "opinion dynamics on correlated subjects in social networks")
  Member "sensor networks with random links topology design for distributed consensus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matcha speeding up decentralized sgd via matching decomposition sampling")
  Member "distributed consensus algorithms in sensor networks quantized data and random link failures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents")

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)