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: "network flow algorithms for structured sparsity"
  Abstract: We consider a class of learning problems that involve a structured sparsity-inducing norm defined as the sum of $\ell_\infty$-norms over groups of variables. Whereas a lot of effort has been put in developing fast optimization methods when the groups are disjoint or embedded in a specific hierarchical structure, we address here the case of general overlapping groups. To this end, we show that the corresponding optimization problem is related to network flow optimization. More precisely, the proximal problem associated with the norm we consider is dual to a quadratic min-cost flow problem. We propose an efficient procedure which computes its solution exactly in polynomial time. Our algorithm scales up to millions of variables, and opens up a whole new range of applications for structured sparse models. We present several experiments on image and video data, demonstrating the applicability and scalability of our approach for various problems.

Connected through hyperedge HE-1 (induced by source paper "regularized loss minimizers with local data perturbation consistency and data irrecoverability"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "learning structural changes of gaussian graphical models in controlled experiments"
    - "calibrating data to sensitivity in private data analysis"
    - "estimation of simultaneously sparse and low rank matrices"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "differentially private empirical risk minimization"
    - "learning exponential families in high dimensions strong convexity and sparsity"

Connected through hyperedge HE-2 (induced by source paper "sparse modeling for image and vision processing"):
  with members:
    - "sparsity based poisson denoising with dictionary learning"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "generalized max pooling"
    - "fast and robust archetypal analysis for representation learning"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "solving oscar regularization problems by proximal splitting algorithms"):
  with members:
    - "structured sparsity through convex optimization"
    - "fast overlapping group lasso"
    - "block sparsity coherence and efficient recovery"

Connected through hyperedge HE-4 (induced by source paper "smoothing proximal gradient method for general structured sparse learning"):
  with members:
    - "fast overlapping group lasso"

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"
    - "structured sparsity inducing norms through submodular functions"

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"
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"
    - "collaborative hierarchical sparse modeling"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-8 (induced by source paper "learning hierarchical and topographic dictionaries with structured sparsity"):
  with members:
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"
    - "collaborative hierarchical sparse modeling"

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)