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: "collaborative hierarchical sparse modeling"
  Abstract: Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is done by solving an l_1-regularized linear regression problem, usually called Lasso. In this work we first combine the sparsity-inducing property of the Lasso model, at the individual feature level, with the block-sparsity property of the group Lasso model, where sparse groups of features are jointly encoded, obtaining a sparsity pattern hierarchically structured. This results in the hierarchical Lasso, which shows important practical modeling advantages. We then extend this approach to the collaborative case, where a set of simultaneously coded signals share the same sparsity pattern at the higher (group) level but not necessarily at the lower one. Signals then share the same active groups, or classes, but not necessarily the same active set. This is very well suited for applications such as source separation. An efficient optimization procedure, which guarantees convergence to the global optimum, is developed for these new models. The underlying presentation of the new framework and optimization approach is complemented with experimental examples and preliminary theoretical results.

Connected through hyperedge HE-1 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements"):
  with members:
    - "low overhead hierarchically sparse channel estimation for multiuser wideband massive mimo"
    - "secure massive iot using hierarchical fast blind deconvolution"
    - "performance of hierarchical sparse detectors for massive mtc"
    - "hierarchical sparse channel estimation for massive mimo"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"

Connected through hyperedge HE-2 (induced by source paper "reliable recovery of hierarchically sparse signals and application in machine type communications"):
  with members:
    - "low rank tensor recovery via iterative hard thresholding"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "collaborative multi sensor classification via sparsity based representation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "structured sparsity through convex optimization"
    - "model based compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "classification with sparse overlapping groups"):
  with members:
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "structured sparsity and generalization"
    - "convex approaches to model wavelet sparsity patterns"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-4 (induced by source paper "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"):
  with members:
    - "a concentration theorem for projections"

Connected through hyperedge HE-5 (induced by source paper "learning with submodular functions a convex optimization perspective"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "maximizing submodular functions using probabilistic graphical models"
    - "reflection methods for user friendly submodular optimization"
    - "convex relaxations of structured matrix factorizations"
    - "duality between subgradient and conditional gradient methods"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "pac learning bounded tree width graphical models"
    - "near optimal nonmyopic value of information in graphical models"

Connected through hyperedge HE-6 (induced by source paper "structured sparsity through convex optimization"):
  with members:
    - "smoothing proximal gradient method for general structured sparse learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "optimization with sparsity inducing penalties"
    - "convex approaches to model wavelet sparsity patterns"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "shaping level sets with submodular functions"
    - "network flow algorithms for structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "optimization with sparsity inducing penalties"):
  with members:
    - "convex approaches to model wavelet sparsity patterns"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "robust pca via outlier pursuit"
    - "shaping level sets with submodular functions"
    - "network flow algorithms for structured sparsity"
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-8 (induced by source paper "learning hierarchical and topographic dictionaries with structured sparsity"):
  with members:
    - "network flow algorithms for structured sparsity"
    - "structured sparsity inducing norms through submodular functions"
    - "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.IT(Information Theory)