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 parametric dictionaries for signals on graphs"
  Abstract: In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties – the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on weighted graphs, an additional design challenge is to incorporate the intrinsic geometric structure of the irregular data domain into the atoms of the dictionary. In this work, we propose a parametric dictionary learning algorithm to design data-adapted, structured dictionaries that sparsely represent graph signals. In particular, we model graph signals as combinations of overlapping local patterns. We impose the constraint that each dictionary is a concatenation of subdictionaries, with each subdictionary being a polynomial of the graph Laplacian matrix, representing a single pattern translated to different areas of the graph. The learning algorithm adapts the patterns to a training set of graph signals. Experimental results on both synthetic and real datasets demonstrate that the dictionaries learned by the proposed algorithm are competitive with and often better than unstructured dictionaries learned by state-of-the-art numerical learning algorithms in terms of sparse approximation of graph signals. In contrast to the unstructured dictionaries, however, the dictionaries learned by the proposed algorithm feature localized atoms and can be implemented in a computationally efficient manner in signal processing tasks such as compression, denoising, and classification.

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 "fractional spectral graph wavelets and their applications"):
  with members:
    - "fractional wavelet scattering network and applications"
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "robust spatial filtering with graph convolutional neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-5 (induced by source paper "graph domain adaptation with localized graph signal representations"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "deep visual domain adaptation a survey"
    - "unsupervised domain adaptation with copula models"
    - "adversarial discriminative domain adaptation"
    - "geometric deep learning going beyond euclidean data"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "learning an invariant hilbert space for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "finding gems multi scale dictionaries for high dimensional graph signals"):
  with members:
    - "structure aware classification using supervised dictionary learning"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "learning laplacian matrix in smooth graph signal representations"
    - "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"
    - "redundant wavelets on graphs and high dimensional data clouds"

Connected through hyperedge HE-7 (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-8 (induced by source paper "isometric transformation invariant graph based deep neural network"):
  with members:
    - "deformable convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "graph based isometry invariant representation learning"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

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)