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 "instance optimal decoding and the restricted isometry property"):
  Abstract: In this paper, we address the question of information preservation in ill-posed, non-linear inverse problems, assuming that the measured data is close to a low-dimensional model set. We provide necessary and sufficient conditions for the existence of a so-called instance optimal decoder, i.e., that is robust to noise and modelling error. Inspired by existing results in compressive sensing, our analysis is based on a (Lower) Restricted Isometry Property (LRIP), formulated in a non-linear fashion. We also provide sufficient conditions for non-uniform recovery with random measurement operators, with a new formulation of the LRIP. We finish by describing typical strategies to prove the LRIP in both linear and non-linear cases, and illustrate our results by studying the invertibility of a one-layer neural net with random weights.

Includes members:
  Member "sketching for large scale learning of mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "deep neural networks with random gaussian weights a universal classification strategy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep randomized neural networks")
  Member "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing")
  Member "on the equivalence between kernel quadrature rules and random feature expansions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional spectral kernel learning")
  Member "representation and coding of signal geometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "fundamental performance limits for ideal decoders in high dimensional linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem")
  Member "sparsity constrained nonlinear optimization optimality conditions and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization")

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)