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: "estimation of simultaneously sparse and low rank matrices"
  Abstract: The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have adjacency matrices which are block-diagonal in the appropriate basis. We introduce a convex mixed penalty which involves $\ell_1$-norm and trace norm simultaneously. We obtain an oracle inequality which indicates how the two effects interact according to the nature of the target matrix. We bound generalization error in the link prediction problem. We also develop proximal descent strategies to solve the optimization problem efficiently and evaluate performance on synthetic and real data sets.

Connected through hyperedge HE-1 (induced by source paper "social network fusion and mining a survey"):
  with members:
    - "bl ecd broad learning based enterprise community detection via hierarchical structure fusion"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
    - "bicycle sharing system analysis and trip prediction"
    - "node2vec scalable feature learning for networks"
    - "organizational chart inference"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (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"
    - "network flow algorithms for structured sparsity"
    - "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-3 (induced by source paper "community detection in partially observable social networks"):
  with members:
    - "provable and practical approximations for the degree distribution using sublinear graph samples"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "structure and overlaps of communities in networks"
    - "defining and evaluating network communities based on ground truth"
    - "finding missing edges and communities in incomplete networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-4 (induced by source paper "recovery of sparse and low rank components of matrices using iterative method with adaptive thresholding"):
  with members:
    - "low rank tensor recovery via iterative hard thresholding"

Connected through hyperedge HE-5 (induced by source paper "enhanced low rank matrix approximation"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "convex denoising using non convex tight frame regularization"
    - "a note on patch based low rank minimization for fast image denoising"
    - "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "patch based low rank minimization for image denoising"

Connected through hyperedge HE-6 (induced by source paper "log normal matrix completion for large scale link prediction"):
  with members:
    - "max margin nonparametric latent feature models for link prediction"
    - "pu learning for matrix completion"
    - "a convex formulation for learning scale free networks via submodular relaxation"
    - "nonparametric link prediction in dynamic networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-7 (induced by source paper "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing of simultaneous low rank and joint sparse matrices"
    - "consistency of trace norm minimization"

Connected through hyperedge HE-8 (induced by source paper "dc proximal newton for non convex optimization problems"):
  with members:
    - "nonconvex regularizations for feature selection in ranking with sparse svm"
    - "low rank structure learning via nonconvex heuristic recovery"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"

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.DS(Data Structures and Algorithms)