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: "lifting l q optimization thresholds"
  Abstract: In this paper we look at a connection between the $\ell_q,0\leq q\leq 1$, optimization and under-determined linear systems of equations with sparse solutions. The case $q=1$, or in other words $\ell_1$ optimization and its a connection with linear systems has been thoroughly studied in last several decades; in fact, especially so during the last decade after the seminal works \cite{CRT,DOnoho06CS} appeared. While current understanding of $\ell_1$ optimization-linear systems connection is fairly known, much less so is the case with a general $\ell_q,0<q<1$, optimization. In our recent work \cite{StojnicLqThrBnds10} we provided a study in this direction. As a result we were able to obtain a collection of lower bounds on various $\ell_q,0\leq q\leq 1$, optimization thresholds. In this paper, we provide a substantial conceptual improvement of the methodology presented in \cite{StojnicLqThrBnds10}. Moreover, the practical results in terms of achievable thresholds are also encouraging. As is usually the case with these and similar problems, the methodology we developed emphasizes their a combinatorial nature and attempts to somehow handle it. Although our results' main contributions should be on a conceptual level, they already give a very strong suggestion that $\ell_q$ optimization can in fact provide a better performance than $\ell_1$, a fact long believed to be true due to a tighter optimization relaxation it provides to the original $\ell_0$ sparsity finding oriented original problem formulation. As such, they in a way give a solid boost to further exploration of the design of the algorithms that would be able to handle $\ell_q,0<q<1$, optimization in a reasonable (if not polynomial) time.

Connected through hyperedge HE-1 (induced by source paper "does ell_p minimization outperform ell_1 minimization"):
  with members:
    - "from denoising to compressed sensing"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing of block sparse positive vectors"):
  with members:
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "regularly random duality"
    - "lifting l 1 optimization strong and sectional thresholds"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "towards a better compressed sensing"):
  with members:
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"
    - "meshes that trap random subspaces"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "message passing algorithms for compressed 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)