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: "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
  Abstract: Functional neuroimaging can measure the brain?s response to an external stimulus. It is used to perform brain mapping: identifying from these observations the brain regions involved. This problem can be cast into a linear supervised learning task where the neuroimaging data are used as predictors for the stimulus. Brain mapping is then seen as a support recovery problem. On functional MRI (fMRI) data, this problem is particularly challenging as i) the number of samples is small due to limited acquisition time and ii) the variables are strongly correlated. We propose to overcome these difficulties using sparse regression models over new variables obtained by clustering of the original variables. The use of randomization techniques, e.g. bootstrap samples, and clustering of the variables improves the recovery properties of sparse methods. We demonstrate the benefit of our approach on an extensive simulation study as well as two fMRI datasets.

Connected through hyperedge HE-1 (induced by source paper "feature grouping as a stochastic regularizer for high dimensional structured data"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "playing atari with deep reinforcement learning"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "structured sparsity through convex optimization"
    - "the numpy array a structure for efficient numerical computation"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-2 (induced by source paper "using feature grouping as a stochastic regularizer for high dimensional noisy data"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "playing atari with deep reinforcement learning"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "structured sparsity through convex optimization"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-3 (induced by source paper "a novel approach for stable selection of informative redundant features from high dimensional fmri data"):
  with members:
    - "randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification"
    - "discovering support and affiliated features from very high dimensions"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-4 (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"
    - "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"
    - "total variation regularization for fmri based prediction of behavior"

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)