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 tight version of the gaussian min max theorem in the presence of convexity"
  Abstract: Gaussian comparison theorems are useful tools in probability theory; they are#R##N#essential ingredients in the classical proofs of many results in empirical#R##N#processes and extreme value theory. More recently, they have been used#R##N#extensively in the analysis of underdetermined linear inverse problems. A#R##N#prominent role in the study of those problems is played by Gordon's Gaussian#R##N#min-max theorem. It has been observed that the use of the Gaussian min-max#R##N#theorem produces results that are often tight. Motivated by recent work due to#R##N#M. Stojnic, we argue explicitly that the theorem is tight under additional#R##N#convexity assumptions. To illustrate the usefulness of the result we provide an#R##N#application example from the field of noisy linear inverse problems.

Connected through hyperedge HE-1 (induced by source paper "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "the gaussian min max theorem in the presence of convexity"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "optimal phase transitions in 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)