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 "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.

Includes members:
  Member "isotropically random orthogonal matrices performance of lasso and minimum conic singular values":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing")
  Member "corrupted sensing novel guarantees for separating structured signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "a framework to characterize performance of lasso algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simple bound on the ber of the map decoder for massive mimo systems")
  Member "the squared error of generalized lasso a precise analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization")
  Member "meshes that trap random subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "precise error analysis of regularized m estimators in high dimensions")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)