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 "controllability of bandlimited graph processes over random time varying graphs"):
  Abstract: Controllability of complex networks arises in many technological problems involving social, financial, road, communication, and smart grid networks. In many practical situations, the underlying topology might change randomly with time, due to link failures such as changing friendships, road blocks or sensor malfunctions. Thus leading to poorly controlled dynamics if the randomness is not properly accounted for. We consider the problem of driving a random time-varying network to a desired state that is graph-bandlimited with respect to the underlying topology, i.e., it has a sparse representation in the graph spectral domain. We first formulate the problem of sparse controllability on deterministic networks and exploit graph signal processing to obtain conditions on the number of required controlled nodes and the final control time. We then move on to random time-varying networks and develop the concept of controllability in the mean to control the expected system towards the desired state. A detailed mean squared analysis is performed to quantify the statistical deviation of a particular random realization of the network. Finally, different sparse control strategies are proposed and their effectiveness is evaluated on synthetic network models and social networks.

Includes members:
  Member "localized linear regression in networked data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning networked exponential families with network lasso")
  Member "greedy sampling of graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural network approximation of graph fourier transforms for sparse sampling of networked flow dynamics")
  Member "adaptive graph signal processing algorithms and optimal sampling strategies":
    also connected through other hyperedges:
      (none)
  Member "autoregressive moving average graph filtering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "filtering random graph processes over random time varying graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tensor graph convolutional networks for multi relational and robust learning")
  Member "rethinking sketching as sampling a graph signal processing approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive graph signal processing algorithms and optimal sampling strategies")
  Member "reconstruction of graph signals through percolation from seeding nodes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "learning heat diffusion graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")

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)