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: "approximation algorithms for model based compressive sensing"
  Abstract: Compressive Sensing (CS) stipulates that a sparse signal can be recovered from a small number of linear measurements, and that this recovery can be performed efficiently in polynomial time. The framework of model-based compressive sensing (model-CS) leverages additional structure in the signal and prescribes new recovery schemes that can reduce the number of measurements even further. However, model-CS requires an algorithm that solves the model-projection problem: given a query signal, produce the signal in the model that is also closest to the query signal. Often, this optimization can be computationally very expensive. Moreover, an approximation algorithm is not sufficient for this optimization task. As a result, the model-projection problem poses a fundamental obstacle for extending model-CS to many interesting models. #R##N#In this paper, we introduce a new framework that we call approximation-tolerant model-based compressive sensing. This framework includes a range of algorithms for sparse recovery that require only approximate solutions for the model-projection problem. In essence, our work removes the aforementioned obstacle to model-based compressive sensing, thereby extending the applicability of model-CS to a much wider class of models. We instantiate this new framework for the Constrained Earth Mover Distance (CEMD) model, which is particularly useful for signal ensembles where the positions of the nonzero coefficients do not change significantly as a function of spatial (or temporal) location. We develop novel approximation algorithms for both the maximization and the minimization versions of the model-projection problem via graph optimization techniques. Leveraging these algorithms into our framework results in a nearly sample-optimal sparse recovery scheme for the CEMD model.

Connected through hyperedge HE-1 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "how to escape saddle points efficiently"
    - "a survey on social media anomaly detection"
    - "unregularized online learning algorithms with general loss functions"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "dual averaging method for online graph structured sparsity"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient regret minimization in non convex games"
    - "graph sparse logistic regression"
    - "learning with feature evolvable streams"
    - "unregularized online learning algorithms with general loss functions"
    - "adam a method for stochastic optimization"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"

Connected through hyperedge HE-3 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  with members:
    - "relating leverage scores and density using regularized christoffel functions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "active regression via linear sample sparsification"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "principal component projection without principal component analysis"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-4 (induced by source paper "thwarting adversarial examples an l_0 robustsparse fourier transform"):
  with members:
    - "the robust manifold defense adversarial training using generative models"
    - "towards evaluating the robustness of neural networks"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "intriguing properties of neural networks"
    - "robust principal component analysis"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "online decomposition of compressive streaming data using n ell_1 cluster weighted minimization"):
  with members:
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressive principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-6 (induced by source paper "a generic framework for interesting subspace cluster detection in multi attributed networks"):
  with members:
    - "convexification of learning from constraints"
    - "outlier detection from network data with subnetwork interpretation"
    - "scalable anomaly ranking of attributed neighborhoods"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-7 (induced by source paper "a fast algorithm for separated sparsity via perturbed lagrangians"):
  with members:
    - "super resolution off the grid"
    - "structured sparsity through convex optimization"
    - "sampling and recovery of pulse streams"
    - "structured compressed sensing from theory to applications"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "on the projection operator to a three view cardinality constrained set"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "structured sparsity through convex optimization"
    - "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)