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: "learning sparse graphs under smoothness prior"
  Abstract: In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we parameterize the graph topology using an edge sampling function. That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which provides an explicit handle to control the sparsity level of the graph. We solve the sparse graph learning problem given some training data in both the noiseless and noisy settings. Given the true smooth data, the posed sparse graph learning problem can be solved optimally and is based on simple rank ordering. Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.

Connected through hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graphsac detecting anomalies in large scale graphs"
    - "power systems topology and state estimation by graph blind source separation"
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "graph signal processing part i graphs graph spectra and spectral clustering"
    - "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
    - "graph signal processing part ii processing and analyzing signals on graphs"
    - "an intuitive derivation of the coherence index relation in compressive sensing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "blind identification of graph filters"
    - "network inference via the time varying graphical lasso"
    - "graph based transform coding with application to image compression"
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-4 (induced by source paper "clustering aware graph construction a joint learning perspective"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "graph learning from data under laplacian and structural constraints"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-5 (induced by source paper "modelling graph errors towards robust graph signal processing"):
  with members:
    - "filtering random graph processes over random time varying graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "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-6 (induced by source paper "learning graphs from linear measurements fundamental trade offs and applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "failure localization in power systems via tree partitions"
    - "learning identifiable gaussian bayesian networks in polynomial time and sample complexity"
    - "exact topology and parameter estimation in distribution grids with minimal observability"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "information theoretic limits of bayesian network structure learning"
    - "on the inverse power flow problem"

Connected through hyperedge HE-7 (induced by source paper "graph learning for spatiotemporal signal with long short term characterization"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "robust principal component analysis on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "blind community detection from low rank excitations of a graph filter"):
  with members:
    - "latent agents in networks estimation and pricing"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "blind identification of graph filters"
    - "autoregressive moving average graph filtering"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "compressive spectral clustering"

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)