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: "total variation regularization for fmri based prediction of behavior"
  Abstract: While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional magnetic resonance imaging (fMRI) data, that provide an indirect measure of task-related or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioral variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the l1 norm of the image gradient, also known as its total variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification.

Connected through hyperedge HE-1 (induced by source paper "analyzing neuroimaging data through recurrent deep learning models"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "methods for interpreting and understanding deep neural networks"
    - "a recurrent convolutional neural network approach for sensorless force estimation in robotic surgery"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-2 (induced by source paper "randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification"):
  with members:
    - "efficient sparse group feature selection via nonconvex optimization"
    - "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
    - "a supervised clustering approach for fmri based inference of brain states"
    - "structured sparsity through convex optimization"
    - "optimization with sparsity inducing penalties"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "group sparse model selection hardness and relaxations"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "convex approaches to model wavelet sparsity patterns"
    - "model based compressive sensing"

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.CV(Computer Vision and Pattern Recognition)