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: "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
  Abstract: Multitask learning can be effective when features useful in one task are also useful for other tasks, and the group lasso is a standard method for selecting a common subset of features. In this paper, we are interested in a less restrictive form of multitask learning, wherein (1) the available features can be organized into subsets according to a notion of similarity and (2) features useful in one task are similar, but not necessarily identical, to the features best suited for other tasks. The main contribution of this paper is a new procedure called Sparse Overlapping Sets (SOS) lasso, a convex optimization that automatically selects similar features for related learning tasks. Error bounds are derived for SOSlasso and its consistency is established for squared error loss. In particular, SOSlasso is motivated by multi- subject fMRI studies in which functional activity is classified using brain voxels as features. Experiments with real and synthetic data demonstrate the advantages of SOSlasso compared to the lasso and group lasso.

Connected through hyperedge HE-1 (induced by source paper "domain independent svm for transfer learning in brain decoding"):
  with members:
    - "learning domain invariant subspace using domain features and independence maximization"
    - "visual domain adaptation with manifold embedded distribution alignment"

Connected through hyperedge HE-2 (induced by source paper "sturm sparse tubal regularized multilinear regression for fmri"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "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"
    - "tensor decompositions a new concept in brain data analysis"
    - "the power and arnoldi methods in an algebra of circulants"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "a survey on multi task learning"):
  with members:
    - "learning to multitask"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "learning multiple visual tasks while discovering their structure"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "safe screening for multi task feature learning with multiple data matrices"

Connected through hyperedge HE-4 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "convex relaxations of structured matrix factorizations"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"
    - "convex approaches to model wavelet sparsity patterns"

Connected through hyperedge HE-5 (induced by source paper "classification with sparse overlapping groups"):
  with members:
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "structured sparsity and generalization"
    - "convex approaches to model wavelet sparsity patterns"
    - "collaborative hierarchical sparse modeling"
    - "self concordant analysis for logistic regression"

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)