human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  Abstract: In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as “atoms” allow us to define “atomic norms” that can be used to formulate convex regularizations for the reconstruction problem. Efficient algorithms are available to solve these formulations in certain special cases, but an approach that works well for general atomic norms, both in terms of speed and reconstruction accuracy, remains to be found. This paper describes an optimization algorithm called CoGEnT that produces solutions with succinct atomic representations for reconstruction problems, generally formulated with atomic-norm constraints. CoGEnT combines a greedy selection scheme based on the conditional gradient approach with a backward (or “truncation”) step that exploits the quadratic nature of the objective to reduce the basis size. We establish convergence properties and validate the algorithm via extensive numerical experiments on a suite of signal processing applications. Our algorithm and analysis also allow for inexact forward steps and for occasional enhancements of the current representation to be performed. CoGEnT can outperform the basic conditional gradient method, and indeed many methods that are tailored to specific applications, when the enhancement and truncation steps are defined appropriately. We also introduce several novel applications that are enabled by the atomic-norm framework, including tensor completion, moment problems in signal processing, and graph deconvolution.

Includes members:
  Member "convexity in source separation models geometry and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning")
  Member "sparse overlapping sets lasso for multitask learning and its application to fmri analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain independent svm for transfer learning in brain decoding")
  Member "convex relaxations of structured matrix factorizations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "binary component decomposition part i the positive semidefinite case")
  Member "solving oscar regularization problems by proximal splitting algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "group sparse matrix recovery")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "block coordinate frank wolfe optimization for structural svms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "high dimensional sparse inverse covariance estimation using greedy methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models")
  Member "orthogonal matching pursuit with replacement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noisy subspace clustering via matching pursuits")

Question: Please tell me which class the source paper 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)