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: "a rigorous geometry probability equivalence in characterization of ell_1 optimization"
  Abstract: In this paper we consider under-determined systems of linear equations that have sparse solutions. This subject attracted enormous amount of interest in recent years primarily due to influential works \cite{CRT,DonohoPol}. In a statistical context it was rigorously established for the first time in \cite{CRT,DonohoPol} that if the number of equations is smaller than but still linearly proportional to the number of unknowns then a sparse vector of sparsity also linearly proportional to the number of unknowns can be recovered through a polynomial $\ell_1$-optimization algorithm (of course, this assuming that such a sparse solution vector exists). Moreover, the geometric approach of \cite{DonohoPol} produced the exact values for the proportionalities in question. In our recent work \cite{StojnicCSetam09} we introduced an alternative statistical approach that produced attainable values of the proportionalities. Those happened to be in an excellent numerical agreement with the ones of \cite{DonohoPol}. In this paper we give a rigorous analytical confirmation that the results of \cite{StojnicCSetam09} indeed match those from \cite{DonohoPol}.

Connected through hyperedge HE-1 (induced by source paper "critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "statistical mechanics approach to 1 bit compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to 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 "the squared error of generalized lasso a precise analysis"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the achievable performance of convex demixing"
    - "living on the edge phase transitions in convex programs with random data"
    - "regularly random duality"
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "a framework to characterize performance of lasso algorithms"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "sharp mse bounds for proximal denoising"

Connected through hyperedge HE-3 (induced by source paper "towards a better compressed sensing"):
  with members:
    - "lifting l q optimization thresholds"
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a framework to characterize performance of lasso algorithms"):
  with members:
    - "upper bounding ell_1 optimization weak thresholds"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "dense error correction via l1 minimization"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "meshes that trap random subspaces"):
  with members:
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "block length dependent thresholds in block sparse compressed sensing"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

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)