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 quasi isometric embeddings with quantized sub gaussian random projections"):
  Abstract: Under which conditions a subset $\mathcal K$ of $\mathbb R^N$ can be embedded in another one of $\delta \mathbb Z^M$ for some resolution $\delta>0$? We address this general question through the specific use of a quantized random linear mapping ${\bf A}:\mathbb R^N \to \delta \mathbb Z^M$ combining a linear projection of $\mathbb R^N$ in $\mathbb R^M$ associated to a random matrix $\boldsymbol \Phi \in \mathbb R^{M\times N}$ with a uniform scalar (dithered) quantization $\mathcal Q$ of $\mathbb R^M$ in $\delta\mathbb Z^M$. The targeted embedding relates the $\ell_2$-distance of any pair of vectors in $\mathcal K$ with the $\ell_1$-distance of their respective mappings in $\delta \mathbb Z^M$, allowing for both multiplicative and additive distortions between these two quantities, i.e., describing a $\ell_2/\ell_1$-quasi-isometric embedding. We show that the sought conditions depend on the Gaussian mean width $w(\mathcal K)$ of the subset $\mathcal K$. In particular, given a symmetric sub-Gaussian distribution $\varphi$ and a precision $\epsilon > 0$, if $M \geq C \epsilon^{-5} w(\mathcal K)^2$ and if the sensing matrix $\boldsymbol \Phi$ has entries i.i.d. as $\varphi$, then, with high probability, the mapping $\bf A$ provides a $\ell_2/\ell_1$-quasi-isometry between $\mathcal K$ and its image in $\delta \mathbb Z^M$. Moreover, in this embedding, the additive distortion is of order $\delta\epsilon$ while the multiplicative one grows with $\epsilon$. For non-Gaussian random $\boldsymbol \Phi$, the multiplicative error is also impacted by the sparsity of the vectors difference, i.e., being smaller for "not too sparse" difference. When $\mathcal K$ is the set of bounded $K$-sparse vectors in any orthonormal basis, then only $M \geq C \epsilon^{-2} \log(c N/K\epsilon^{3/2})$ measurements suffice. Remark: all values $C,c>0$ above only depend on $\delta$ and on the distribution $\varphi$.

Includes members:
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "one bit compressed sensing with non gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")

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)