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: "subsampling for graph power spectrum estimation"
  Abstract: In this paper we focus on subsampling stationary random processes that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

Connected through hyperedge HE-1 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph"):
  with members:
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-2 (induced by source paper "graph u nets"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "large scale learnable graph convolutional networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "predicting multicellular function through multi layer tissue networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "efficient and invariant convolutional neural networks for dense prediction"

Connected through hyperedge HE-3 (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"
    - "stationary time vertex signal processing"

Connected through hyperedge HE-4 (induced by source paper "greedy sampling of graph signals"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "adaptive least mean squares estimation of graph signals"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-5 (induced by source paper "kernel regression for signals over graphs"):
  with members:
    - "stationary signal processing on graphs"
    - "hilbert transform analytic signal and modulation analysis for graph signal processing"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "graph learning from data under laplacian and structural constraints"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-6 (induced by source paper "stationary graph processes and spectral estimation"):
  with members:
    - "blind identification of graph filters"
    - "signal processing on graphs causal modeling of un structured data"
    - "stationary signal processing on graphs"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "distributed linear network operators using graph filters"
    - "distributed autoregressive moving average graph filters"

Connected through hyperedge HE-7 (induced by source paper "graph sampling with determinantal processes"):
  with members:
    - "greedy sampling of graph signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "signals on graphs uncertainty principle and sampling"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "a multiscale pyramid transform for graph signals"

Connected through hyperedge HE-8 (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"
    - "stationary time vertex signal processing"
    - "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"

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.IT(Information Theory)