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 "randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification"):
  Abstract: In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high dimensional voxel space and few training samples, resulting in unreliable feature selection. In order to deal with the difficulty, stability selection has received a great deal of attention lately, especially due to its finite sample control of false discoveries and transparent principle for choosing a proper amount of regularization. However, it fails to make explicit use of the correlation property or structural information of these discriminative features and leads to large false negative rates. In other words, many relevant but probably correlated discriminative voxels are missed. Thus, we propose a new variant on stability selection "randomized structural sparsity", which incorporates the idea of structural sparsity. Numerical experiments demonstrate that our method can be superior in controlling for false negatives while also keeping the control of false positives inherited from stability selection.

Includes members:
  Member "efficient sparse group feature selection via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust subspace clustering via tighter rank approximation")
  Member "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feature grouping as a stochastic regularizer for high dimensional structured data")
  Member "a supervised clustering approach for fmri based inference of brain states":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling brain networks with artificial neural networks")
  Member "structured sparsity through convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint group feature selection and discriminative filter learning for robust visual object tracking")
  Member "optimization with sparsity inducing penalties":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse optimization for green edge ai inference")
  Member "convergence rates of inexact proximal gradient methods for convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "credit assignment techniques in stochastic computation graphs")
  Member "total variation regularization for fmri based prediction of behavior":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analyzing neuroimaging data through recurrent deep learning models")

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)