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: "forest sparsity for multi channel compressive sensing"
  Abstract: In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated. Therefore, the full data could follow the forest structure and we call this property forest sparsity. It exploits both intra- and inter- channel correlations and enriches the family of existing model-based compressive sensing theories. The proposed theory indicates that only   ${\cal O}(Tk+\log (N/k))$   measurements are required for multi-channel data with forest sparsity, where    $T$   is the number of channels,   $N$   and   $k$   are the length and sparsity number of each channel, respectively. This result is much better than   ${\cal O}(Tk+T\log (N/k))$   of tree sparsity,   ${\cal O}(Tk+k\log (N/k))$   of joint sparsity, and far better than   ${\cal O}(Tk+Tk\log (N/k))$   of standard sparsity. In addition, we extend the forest sparsity theory to the multiple measurement vectors problem, where the measurement matrix is a block-diagonal matrix. The result shows that the required measurement bound can be the same as that for dense random measurement matrix, when the data shares equal energy in each channel. A new algorithm is developed and applied on four example applications to validate the benefit of the proposed model. Extensive experiments demonstrate the effectiveness and efficiency of the proposed theory and algorithm.

Connected through hyperedge HE-1 (induced by source paper "a divide and conquer approach to compressed sensing mri"):
  with members:
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "bayesian nonparametric dictionary learning for compressed sensing mri"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"):
  with members:
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

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)