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 "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  Abstract: Recently, many works have focused on the characterization of non-linear dimensionality reduction methods obtained by quantizing linear embeddings, e.g., to reach fast processing time, efficient data compression procedures, novel geometry-preserving embeddings or to estimate the information/bits stored in this reduced data representation. In this work, we prove that many linear maps known to respect the restricted isometry property (RIP) can induce a quantized random embedding with controllable multiplicative and additive distortions with respect to the pairwise distances of the data points beings considered. In other words, linear matrices having fast matrix-vector multiplication algorithms (e.g., based on partial Fourier ensembles or on the adjacency matrix of unbalanced expanders) can be readily used in the definition of fast quantized embeddings with small distortions. This implication is made possible by applying right after the linear map an additive and random "dither" that stabilizes the impact of the uniform scalar quantization operator applied afterwards. For different categories of RIP matrices, i.e., for different linear embeddings of a metric space $(\mathcal K \subset \mathbb R^n, \ell_q)$ in $(\mathbb R^m, \ell_p)$ with $p,q \geq 1$, we derive upper bounds on the additive distortion induced by quantization, showing that it decays either when the embedding dimension $m$ increases or when the distance of a pair of embedded vectors in $\mathcal K$ decreases. Finally, we develop a novel "bi-dithered" quantization scheme, which allows for a reduced distortion that decreases when the embedding dimension grows and independently of the considered pair of vectors.

Includes members:
  Member "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "through the haze a non convex approach to blind gain calibration for linear random sensing models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem")
  Member "near optimal sample complexity bounds for circulant binary embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in convolutional neural network acceleration")
  Member "an overview of low rank matrix recovery from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "recipes for stable linear embeddings from hilbert spaces to ℝ m":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "functional nonlinear sparse models")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "isometric sketching of any set via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "representation and coding of signal geometry":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")

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)