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: "stationary time vertex signal processing"
  Abstract: This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some {known} graph topology. We put forth a new definition of time-vertex wide-sense stationarity, or joint stationarity for short, that goes beyond product graphs. Joint stationarity helps by reducing the estimation variance and recovery complexity. In particular, for any jointly stationary process (a) one reliably learns the covariance structure from as little as a single realization of the process, and (b) solves MMSE recovery problems, such as interpolation and denoising, in computational time nearly linear on the number of edges and timesteps. Experiments with three datasets suggest that joint stationarity can yield accuracy improvements in the recovery of high-dimensional processes evolving over a graph, even when the latter is only approximately known, or the process is not strictly stationary.

Connected through hyperedge HE-1 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs"):
  with members:
    - "an intuitive derivation of the coherence index relation in compressive sensing"
    - "graph filters and the z laplacian"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "hilbert transform analytic signal and modulation analysis for graph signal processing"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-2 (induced by source paper "stationary signal processing on graphs"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "fast robust pca on graphs"
    - "towards stationary time vertex signal processing"
    - "subsampling for graph power spectrum estimation"
    - "a probabilistic interpretation of sampling theory of graph signals"
    - "gspbox a toolbox for signal processing on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"

Connected through hyperedge HE-3 (induced by source paper "multilinear low rank tensors on graphs applications"):
  with members:
    - "fast robust pca on graphs"
    - "fast eigenspace approximation using random signals"
    - "compressive pca for low rank matrices on graphs"
    - "convolutional neural networks with low rank regularization"
    - "robust principal component analysis on graphs"
    - "fast detection of overlapping communities via online tensor methods"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "towards stationary time vertex signal processing"):
  with members:
    - "autoregressive moving average graph filtering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "frequency analysis of temporal graph signals"
    - "signal processing on graphs modeling causal relations in big data"
    - "gspbox a toolbox for signal processing on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

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.LG(Machine Learning)