human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "stability of graph neural networks to relative perturbations"):
  with members:
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "gated graph convolutional recurrent neural networks"
    - "stability properties of graph neural networks"
    - "graph convolutional neural networks via scattering"
    - "diffusion scattering transforms on graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "network topology inference from spectral templates"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "rethinking sketching as sampling a graph signal processing approach"):
  with members:
    - "greedy sampling of graph signals"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive pca for low rank matrices on graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Question: Please tell me which class the center node 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)