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: "stable robust and super fast reconstruction of tensors using multi way projections"
  Abstract: In the framework of multidimensional Compressed Sensing (CS), we introduce an analytical reconstruction formula that allows one to recover an   $N$  th-order data tensor   $\underline{\bf X}\in{\BBR^{I_{1}\times I_{2}\times\cdots\times I_{N}}}$   from a reduced set of multi-way compressive measurements by exploiting its low multilinear-rank structure. Moreover, we show that, an interesting property of multi-way measurements allows us to build the reconstruction based on compressive linear measurements taken only in two selected modes, independently of the tensor order   $N$  . In addition, it is proved that, in the matrix case and in a particular case with 3rd-order tensors where the same 2D sensor operator is applied to all mode-3 slices, the proposed reconstruction   $\underline{\bf X}_{\tau}$   is stable in the sense that the approximation error is comparable to the one provided by the best low-multilinear-rank approximation, where   $\tau$   is a threshold parameter that controls the approximation error. Through the analysis of the upper bound of the approximation error we show that, in the 2D case, an optimal value for the threshold parameter    $\tau=\tau_{0}>0$   exists, which is confirmed by our simulation results. On the other hand, our experiments on 3D datasets show that very good reconstructions are obtained using    $\tau=0$  , which means that this parameter does not need to be tuned. Our extensive simulation results demonstrate the stability and robustness of the method when it is applied to real-world 2D and 3D signals. A comparison with state-of-the-arts sparsity based CS methods specialized for multidimensional signals is also included. A very attractive characteristic of the proposed method is that it provides a direct computation, i.e., it is non-iterative in contrast to all existing sparsity based CS algorithms, thus providing super fast computations, even for large datasets.

Connected through hyperedge HE-1 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-2 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-3 (induced by source paper "fast greedy approaches for compressive sensing of large scale signals"):
  with members:
    - "separable dictionary learning"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

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.IT(Information Theory)