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: "graph learning under sparsity priors"
  Abstract: Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred from the mere signal observations. This is exactly the problem that we address in this paper, under the assumption that the graph signals can be represented as a sparse linear combination of a few atoms of a structured graph dictionary. The dictionary is constructed on polynomials of the graph Laplacian, which can sparsely represent a general class of graph signals composed of localized patterns on the graph. We formulate a graph learning problem, whose solution provides an ideal fit between the signal observations and the sparse graph signal model. As the problem is non-convex, we propose to solve it by alternating between a signal sparse coding and a graph update step. We provide experimental results that outline the good graph recovery performance of our method, which generally compares favourably to other recent network inference algorithms.

Connected through hyperedge HE-1 (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"
    - "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"
    - "learning heat diffusion graphs"

Connected through hyperedge HE-2 (induced by source paper "graph heat mixture model learning"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graph laplacian mixture model"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "graph learning from data under laplacian and structural constraints"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-3 (induced by source paper "graph laplacian mixture model"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "learning laplacian matrix in smooth graph signal representations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-4 (induced by source paper "deep neural networks on graph signals for brain imaging analysis"):
  with members:
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "simultaneous low rank component and graph estimation for high dimensional graph signals application to brain imaging"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-5 (induced by source paper "characterization and inference of graph diffusion processes from observations of stationary signals"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "network scale traffic modeling and forecasting with graphical lasso and neural networks"
    - "graph reconstruction from the observation of diffused signals"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "network topology identification from spectral templates"
    - "learning sparse graphs under smoothness prior"

Connected through hyperedge HE-6 (induced by source paper "characterization and inference of weighted graph topologies from observations of diffused signals"):
  with members:
    - "stationary signal processing on graphs"
    - "network scale traffic modeling and forecasting with graphical lasso and neural networks"
    - "graph reconstruction from the observation of diffused signals"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "learning sparse graphs under smoothness prior"
    - "learning laplacian matrix in smooth graph signal representations"
    - "discrete signal processing on graphs frequency analysis"

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)