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: "robust principal component analysis on graphs"
  Abstract: Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first issue with a sparse penalty term. The second issue can be handled with the matrix factorization model, which is however non-convex. Besides, PCA based clustering can also be enhanced by using a graph of data similarity. In this article, we introduce a new model called "Robust PCA on Graphs" which incorporates spectral graph regularization into the Robust PCA framework. Our proposed model benefits from 1) the robustness of principal components to occlusions and missing values, 2) enhanced low-rank recovery, 3) improved clustering property due to the graph smoothness assumption on the low-rank matrix, and 4) convexity of the resulting optimization problem. Extensive experiments on 8 benchmark, 3 video and 2 artificial datasets with corruptions clearly reveal that our model outperforms 10 other state-of-the-art models in its clustering and low-rank recovery tasks.

Connected through hyperedge HE-1 (induced by source paper "wasserstein based graph alignment"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "netlsd hearing the shape of a graph"
    - "learning permutations with sinkhorn policy gradient"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"
    - "learning laplacian matrix in smooth graph signal representations"

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 "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"
    - "learning sparse graphs under smoothness prior"
    - "learning laplacian matrix in smooth graph signal representations"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-4 (induced by source paper "robust graph learning from noisy data"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "unsupervised feature selection with adaptive structure learning"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-5 (induced by source paper "got an optimal transport framework for graph comparison"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "gromov wasserstein alignment of word embedding spaces"
    - "netlsd hearing the shape of a graph"
    - "implicit reparameterization gradients"
    - "learning permutations with sinkhorn policy gradient"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"

Connected through hyperedge HE-6 (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"
    - "learning sparse graphs under smoothness prior"
    - "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-7 (induced by source paper "matrix normal pca for interpretable dimension reduction and graphical noise modeling"):
  with members:
    - "bayesian joint matrix decomposition for data integration with heterogeneous noise"
    - "improved training of wasserstein gans"
    - "robust recovery of subspace structures by low rank representation"
    - "residual component analysis generalising pca for more flexible inference in linear gaussian models"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "guaranteed matrix completion via non convex factorization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

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.CV(Computer Vision and Pattern Recognition)