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 "under determined linear systems and l q optimization thresholds"):
  Abstract: Recent studies of under-determined linear systems of equations with sparse solutions showed a great practical and theoretical efficiency of a particular technique called $\ell_1$-optimization. Seminal works \cite{CRT,DOnoho06CS} rigorously confirmed it for the first time. Namely, \cite{CRT,DOnoho06CS} showed, in a statistical context, that $\ell_1$ technique can recover sparse solutions of under-determined systems even when the sparsity is linearly proportional to the dimension of the system. A followup \cite{DonohoPol} then precisely characterized such a linearity through a geometric approach and a series of work\cite{StojnicCSetam09,StojnicUpper10,StojnicEquiv10} reaffirmed statements of \cite{DonohoPol} through a purely probabilistic approach. A theoretically interesting alternative to $\ell_1$ is a more general version called $\ell_q$ (with an essentially arbitrary $q$). While $\ell_1$ is typically considered as a first available convex relaxation of sparsity norm $\ell_0$, $\ell_q,0\leq q\leq 1$, albeit non-convex, should technically be a tighter relaxation of $\ell_0$. Even though developing polynomial (or close to be polynomial) algorithms for non-convex problems is still in its initial phases one may wonder what would be the limits of an $\ell_q,0\leq q\leq 1$, relaxation even if at some point one can develop algorithms that could handle its non-convexity. A collection of answers to this and a few realted questions is precisely what we present in this paper.

Includes members:
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "various thresholds for ell_1 optimization in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "algorithmic linear dimension reduction in the l_1 norm for sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "group testing an information theory perspective")

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)