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 "small width low distortions quantized random embeddings of low complexity sets"):
  Abstract: Under which conditions and with which distortions can we preserve the pairwise-distances of low-complexity vectors, e.g., for structured sets such as the set of sparse vectors or the one of low-rank matrices, when these are mapped in a finite set of vectors? This work addresses this general question through the specific use of a quantized and dithered random linear mapping which combines, in the following order, a sub-Gaussian random projection in $\mathbb R^M$ of vectors in $\mathbb R^N$, a random translation, or "dither", of the projected vectors and a uniform scalar quantizer of resolution $\delta>0$ applied componentwise. Thanks to this quantized mapping we are first able to show that, with high probability, an embedding of a bounded set $\mathcal K \subset \mathbb R^N$ in $\delta \mathbb Z^M$ can be achieved when distances in the quantized and in the original domains are measured with the $\ell_1$- and $\ell_2$-norm, respectively, and provided the number of quantized observations $M$ is large before the square of the "Gaussian mean width" of $\mathcal K$. In this case, we show that the embedding is actually "quasi-isometric" and only suffers of both multiplicative and additive distortions whose magnitudes decrease as $M^{-1/5}$ for general sets, and as $M^{-1/2}$ for structured set, when $M$ increases. Second, when one is only interested in characterizing the maximal distance separating two elements of $\mathcal K$ mapped to the same quantized vector, i.e., the "consistency width" of the mapping, we show that for a similar number of measurements and with high probability this width decays as $M^{-1/4}$ for general sets and as $1/M$ for structured ones when $M$ increases. Finally, as an important aspect of our work, we also establish how the non-Gaussianity of the mapping impacts the class of vectors that can be embedded or whose consistency width provably decays when $M$ increases.

Includes members:
  Member "compressive classification and the rare eclipse problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets")
  Member "near optimal sample complexity bounds for circulant binary embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances in convolutional neural network acceleration")
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "on the gap between rip properties and sparse recovery conditions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "near optimal bounds for binary embeddings of arbitrary sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one bit sensing of low rank and bisparse matrices")
  Member "dimensionality reduction with subgaussian matrices a unified theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "error decay of almost consistent signal estimations from quantized random gaussian projections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property")
  Member "error bounds for consistent reconstruction random polytopes and coverage processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memoryless scalar quantization for random frames")

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)