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 "improved algorithms for structured sparse recovery"):
  Abstract: It is known that certain structures of the signal in addition to the standard notion of sparsity (called structured sparsity) can improve the sample complexity in several compressive sensing applications. Recently, Hegde et al. proposed a framework, called approximation-tolerant model-based compressive sensing, for recovering signals with structured sparsity. Their framework requires two oracles, the head- and the tail-approximation projection oracles. The two oracles should return approximate solutions in the model which is closest to the query signal. In this paper, we consider two structured sparsity models and obtain improved projection algorithms. The first one is the tree sparsity model, which captures the support structure in the wavelet decomposition of piecewise-smooth signals. We propose a linear time $(1-\epsilon)$-approximation algorithm for head-approximation projection and a linear time $(1+\epsilon)$-approximation algorithm for tail-approximation projection. The best previous result is an $\tilde{O}(n\log n)$ time bicriterion approximation algorithm (meaning that their algorithm may return a solution of sparsity larger than $k$) by Hegde et al. Our result provides an affirmative answer to the open problem mentioned in the survey of Hegde and Indyk. As a corollary, we can recover a constant approximate $k$-sparse signal. The other is the Constrained Earth Mover Distance (CEMD) model, which is useful to model the situation where the positions of the nonzero coefficients of a signal do not change significantly as a function of spatial (or temporal) locations. We obtain the first single criterion constant factor approximation algorithm for the head-approximation projection. The previous best known algorithm is a bicriterion approximation. Using this result, we can get a faster constant approximation algorithm with fewer measurements for the recovery problem in CEMD model.

Includes members:
  Member "approximation algorithms for model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization")
  Member "faster all pairs shortest paths via circuit complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "querying a matrix through matrix vector products")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "modified cs modifying compressive sensing for problems with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilinear compressive learning with prior knowledge")

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.IT(Information Theory)