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 "the gaussian min max theorem in the presence of convexity"):
  Abstract: Gaussian comparison theorems are useful tools in probability theory; they are essential ingredients in the classical proofs of many results in empirical processes and extreme value theory. More recently, they have been used extensively in the analysis of non-smooth optimization problems that arise in the recovery of structured signals from noisy linear observations. We refer to such problems as Primary Optimization (PO) problems. A prominent role in the study of the (PO) problems is played by Gordon's Gaussian min-max theorem (GMT) which provides probabilistic lower bounds on the optimal cost via a simpler Auxiliary Optimization (AO) problem. Motivated by resent work of M. Stojnic, we show that under appropriate convexity assumptions the (AO) problem allows one to tightly bound both the optimal cost, as well as the norm of the solution of the (PO). As an application, we use our result to develop a general framework to tightly characterize the performance (e.g. squared-error) of a wide class of convex optimization algorithms used in the context of noisy signal recovery.

Includes members:
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  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 "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "simple bounds for noisy linear inverse problems with exact side information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sharp time data tradeoffs for linear inverse problems")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "a framework to characterize performance of lasso algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization")
  Member "upper bounding ell_1 optimization weak thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "precise error analysis of regularized m estimators in high dimensions")

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)