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: "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"
  Abstract: Abstract   With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulant graphs which extends basic properties of Finite Rate of Innovation (FRI) theory to the graph domain, and can be applied to arbitrary graphs via suitable approximation schemes. At its core, the introduced Graph-FRI-framework states that any K-sparse signal on the vertices of a circulant graph can be perfectly reconstructed from its dimensionality-reduced representation in the graph spectral domain, the Graph Fourier Transform (GFT), of minimum size 2K. By leveraging the recently developed theory of e-splines and e-spline wavelets on graphs, one can decompose this graph spectral transformation into the multiresolution low-pass filtering operation with a graph e-spline filter, with subsequent transformation to the spectral graph domain; this allows to infer a distinct sampling pattern, and, ultimately, the structure of an associated coarsened graph, which preserves essential properties of the original, including circularity and, where applicable, the graph generating set.

Connected through hyperedge HE-1 (induced by source paper "detecting localized categorical attributes on graphs"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "distributed hybrid power state estimation under pmu sampling phase errors"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "signal representations on graphs tools and applications"
    - "local set based graph signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "splines and wavelets on circulant graphs"):
  with members:
    - "estimation of laplacian spectra of direct and strong product graphs"
    - "gspbox a toolbox for signal processing on graphs"
    - "discrete signal processing on graphs frequency analysis"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"
    - "variational splines and paley wiener spaces on combinatorial graphs"

Connected through hyperedge HE-3 (induced by source paper "hilbert transform analytic signal and modulation analysis for graph signal processing"):
  with members:
    - "splines and wavelets on circulant graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "signals on graphs uncertainty principle and sampling"
    - "robust principal component analysis on graphs"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-4 (induced by source paper "localization decomposition and dictionary learning of piecewise constant signals on graphs"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "signal recovery on graphs variation minimization"
    - "learning parametric dictionaries for signals 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.DM(Discrete Mathematics)