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: "clustering multi way data a novel algebraic approach"
  Abstract: In this paper, we develop a method for unsupervised clustering of two- way (matrix) data by combining two recent innovations from dierent elds: the Sparse Subspace Clustering (SSC) algorithm (10), which groups points coming from a union of subspaces into their respective subspaces, and the t-product (18), which was introduced to provide a matrix-like multiplication for third order tensors. Our algorithm is analogous to SSC in that an \anity" between dierent data points is built using a sparse self-representation of the data. Unlike SSC, we employ the t-product in the self-representation. This allows us more exibility in modeling; in fact, SSC is a special case of our method. When using the t-product, three-way arrays are treated as matrices whose ele- ments (scalars) are n-tuples or tubes. Convolutions take the place of scalar multi- plication. This framework allows us to embed the 2-D data into a vector-space-like structure called a free module over a commutative ring. These free modules retain many properties of complex inner-product spaces, and we leverage that to provide theoretical guarantees on our algorithm. We show that compared to vector-space counterparts, SSmC achieves higher accuracy and better able to cluster data with less preprocessing in some image clustering problems. In particular we show the performance of the proposed method on Weizmann face database, the Extended Yale B Face database and the MNIST handwritten digits database.

Connected through hyperedge HE-1 (induced by source paper "fast randomized algorithms for t product based tensor operations and decompositions with applications to imaging data"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "tensor sparse and low rank based submodule clustering method for multi way data"
    - "on unifying multi view self representations for clustering by tensor multi rank minimization"
    - "exact tensor completion using t svd"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"

Connected through hyperedge HE-2 (induced by source paper "fourth order tensors with multidimensional discrete transforms"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "graph regularized tensor sparse coding for image representation"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor ring decomposition"
    - "on the expressive power of deep learning a tensor analysis"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"

Connected through hyperedge HE-3 (induced by source paper "low rank multi view clustering in third order tensor space"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "tensor sparse and low rank based submodule clustering method for multi way data"
    - "robust and efficient subspace segmentation via least squares regression"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "novel factorization strategies for higher order tensors implications for compression and recovery of multi linear data"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "tensor sparse and low rank based submodule clustering method for multi way data"):
  with members:
    - "group invariant subspace clustering"
    - "a new low rank tensor model for video completion"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-5 (induced by source paper "exact tensor completion using t svd"):
  with members:
    - "adaptive sampling of rf fingerprints for fine grained indoor localization"
    - "incoherence optimal matrix completion"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "a new convex relaxation for tensor completion"
    - "the power and arnoldi methods in an algebra of circulants"
    - "recovering low rank matrices from few coefficients in any basis"
    - "note on sampling without replacing from a finite collection of matrices"

Connected through hyperedge HE-6 (induced by source paper "group invariant subspace clustering"):
  with members:
    - "subspace clustering via thresholding and spectral clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

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)