human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sturm sparse tubal regularized multilinear regression for fmri"):
  Abstract: While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available. Recent sparse multilinear regression methods based on tensor are emerging as promising solutions for fMRI, yet existing works rely on unfolding/folding operations and a tensor rank relaxation with limited tightness. The newly proposed tensor singular value decomposition (t-SVD) sheds light on new directions. In this work, we study t-SVD for sparse multilinear regression and propose a Sparse tubal-regularized multilinear regression (Sturm) method for fMRI. Specifically, the Sturm model performs multilinear regression with two regularization terms: a tubal tensor nuclear norm based on t-SVD and a standard L1 norm. We further derive the algorithm under the alternating direction method of multipliers framework. We perform experiments on four classification problems, including both resting-state fMRI for disease diagnosis and task-based fMRI for neural decoding. The results show the superior performance of Sturm in classifying fMRI using just a small number of voxels.

Includes members:
  Member "boosted sparse and low rank tensor regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tensor regression using low rank and sparse tucker decompositions")
  Member "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm")
  Member "exact tensor completion using t svd":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low rank tensor grid for image completion")
  Member "novel methods for multilinear data completion and de noising based on tensor svd":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor grid for image completion")
  Member "a new convex relaxation for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "sparse overlapping sets lasso for multitask learning and its application to fmri analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain independent svm for transfer learning in brain decoding")
  Member "tensor decompositions a new concept in brain data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient structure preserving support tensor train machine")
  Member "the power and arnoldi methods in an algebra of circulants":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalized visual information analysis via tensorial algebra")

Question: Please tell me which class the source paper 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)