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: "simultaneous low rank component and graph estimation for high dimensional graph signals application to brain imaging"
  Abstract: We propose an algorithm to uncover the intrinsic low-rank component of a high-dimensional, graph-smooth and grossly-corrupted dataset, under the situations that the underlying graph is unknown. Based on a model with a low-rank component plus a sparse perturbation, and an initial graph estimation, our proposed algorithm simultaneously learns the low-rank component and refines the graph. Our evaluations using synthetic and real brain imaging data in unsupervised and supervised classification tasks demonstrate encouraging performance.

Connected through hyperedge HE-1 (induced by source paper "spectral graph wavelet transform as feature extractor for machine learning in neuroimaging"):
  with members:
    - "evaluating graph signal processing for neuroimaging through classification and dimensionality reduction"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

Connected through hyperedge HE-2 (induced by source paper "buildup of speaking skills in an online learning community a network analytic exploration"):
  with members:
    - "graph based compression of dynamic 3d point cloud sequences"
    - "graph laplacian regularization for inverse imaging analysis in the continuous domain"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "learning about social learning in moocs from statistical analysis to generative model"

Connected through hyperedge HE-3 (induced by source paper "deep neural networks on graph signals for brain imaging analysis"):
  with members:
    - "graph learning under sparsity priors"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "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-4 (induced by source paper "evaluating graph signal processing for neuroimaging through classification and dimensionality reduction"):
  with members:
    - "blind identification of graph filters"
    - "random sampling of bandlimited signals on graphs"
    - "gspbox a toolbox for signal processing on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-5 (induced by source paper "closing the knowledge gap in an online learning community network analytic discoveries simulation and prediction"):
  with members:
    - "graph based compression of dynamic 3d point cloud sequences"
    - "graph laplacian regularization for inverse imaging analysis in the continuous domain"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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)