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: "boosted sparse and low rank tensor regression"
  Abstract: We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse. This structure is both parsimonious and highly interpretable, as it implies that the outcome is related to the features through a few distinct pathways, each of which may only involve subsets of feature dimensions. We take a divide-and-conquer strategy to simplify the task into a set of sparse unit-rank tensor regression problems. To make the computation efficient and scalable, for the unit-rank tensor regression, we propose a stagewise estimation procedure to efficiently trace out its entire solution path. We show that as the step size goes to zero, the stagewise solution paths converge exactly to those of the corresponding regularized regression. The superior performance of our approach is demonstrated on various real-world and synthetic examples.

Connected through hyperedge HE-1 (induced by source paper "tensor regression using low rank and sparse tucker decompositions"):
  with members:
    - "learning from multiway data simple and efficient tensor regression"
    - "low rank tensor recovery via iterative hard thresholding"
    - "lower bounds for sparse recovery"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
    - "guaranteed rank minimization via singular value projection"
    - "most tensor problems are np hard"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "sparse and low rank tensor regression via parallel proximal method"):
  with members:
    - "sturm sparse tubal regularized multilinear regression for fmri"
    - "tensor regression meets gaussian processes"
    - "learning from multiway data simple and efficient tensor regression"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "most tensor problems are np hard"

Connected through hyperedge HE-3 (induced by source paper "fast and scalable estimator for sparse and unit rank higher order regression models"):
  with members:
    - "sturm sparse tubal regularized multilinear regression for fmri"
    - "tensor regression meets gaussian processes"
    - "learning from multiway data simple and efficient tensor regression"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-4 (induced by source paper "sturm sparse tubal regularized multilinear regression for fmri"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "tensor decompositions a new concept in brain data analysis"
    - "the power and arnoldi methods in an algebra of circulants"
    - "robust principal component analysis"

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)