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: "filtering random graph processes over random time varying graphs"
  Abstract: Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity in both the graph topology and the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response and autoregressive moving average graph filters, when operating on random time-varying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that 1) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and 2) there are meaningful upper bounds for the variance of the filter output. We conclude this paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm and yield a (up to) four times complexity reduction, with a very little difference from the optimal solution.

Connected through hyperedge HE-1 (induced by source paper "tensor graph convolutional networks for multi relational and robust learning"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "real time power system state estimation and forecasting via deep unrolled neural networks"
    - "gated graph convolutional recurrent neural networks"
    - "adversarial attack on graph structured data"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "transferability of spectral graph convolutional neural networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "stability of graph scattering transforms"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "diffusion scattering transforms on graphs"
    - "autoregressive moving average graph filtering"

Connected through hyperedge HE-3 (induced by source paper "controllability of bandlimited graph processes over random time varying graphs"):
  with members:
    - "localized linear regression in networked data"
    - "greedy sampling of graph signals"
    - "adaptive graph signal processing algorithms and optimal sampling strategies"
    - "autoregressive moving average graph filtering"
    - "rethinking sketching as sampling a graph signal processing approach"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "learning heat diffusion graphs"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-4 (induced by source paper "modelling graph errors towards robust graph signal processing"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "learning sparse graphs under smoothness prior"
    - "sampling of graph signals with successive local aggregations"
    - "discrete signal processing on graphs sampling theory"
    - "stability and continuity of centrality measures in weighted graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-5 (induced by source paper "on the transferability of spectral graph filters"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "a comprehensive survey on graph neural networks"
    - "autoregressive moving average graph filtering"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)