human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
  Abstract: In 1733, Georges-Louis Leclerc, Comte de Buffon in France, set the ground of geometric probability theory by defining an enlightening problem: What is the probability that a needle thrown randomly on a ground made of equispaced parallel strips lies on two of them? In this work, we show that the solution to this problem, and its generalization to $N$ dimensions, allows us to discover a quantized form of the Johnson-Lindenstrauss (JL) Lemma, i.e., one that combines a linear dimensionality reduction procedure with a uniform quantization of precision $\delta>0$. In particular, given a finite set $\mathcal S \subset \mathbb R^N$ of $S$ points and a distortion level $\epsilon>0$, as soon as $M > M_0 = O(\epsilon^{-2} \log S)$, we can (randomly) construct a mapping from $(\mathcal S, \ell_2)$ to $(\delta\mathbb Z^M, \ell_1)$ that approximately preserves the pairwise distances between the points of $\mathcal S$. Interestingly, compared to the common JL Lemma, the mapping is quasi-isometric and we observe both an additive and a multiplicative distortions on the embedded distances. These two distortions, however, decay as $O(\sqrt{(\log S)/M})$ when $M$ increases. Moreover, for coarse quantization, i.e., for high $\delta$ compared to the set radius, the distortion is mainly additive, while for small $\delta$ we tend to a Lipschitz isometric embedding. Finally, we prove the existence of a "nearly" quasi-isometric embedding of $(\mathcal S, \ell_2)$ into $(\delta\mathbb Z^M, \ell_2)$. This one involves a non-linear distortion of the $\ell_2$-distance in $\mathcal S$ that vanishes for distant points in this set. Noticeably, the additive distortion in this case is slower, and decays as $O(\sqrt[4]{(\log S)/M})$.

Connected through hyperedge HE-1 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "bounds on the number of measurements for reliable compressive classification"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "quantization and compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

Connected through hyperedge HE-3 (induced by source paper "phasemax convex phase retrieval via basis pursuit"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase retrieval via reweighted wirtinger flow"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "non convex phase retrieval from stft measurements"
    - "corruption robust phase retrieval via linear programming"

Connected through hyperedge HE-4 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the gap between rip properties and sparse recovery conditions"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "quantization and compressive sensing"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

Connected through hyperedge HE-5 (induced by source paper "quantization and compressive sensing"):
  with members:
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "small width low distortions quantized random embeddings of low complexity sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"

Connected through hyperedge HE-7 (induced by source paper "representation and coding of signal geometry"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "binary embedding fundamental limits and fast algorithm"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-8 (induced by source paper "error decay of almost consistent signal estimations from quantized gaussian random projections"):
  with members:
    - "quantization and compressive sensing"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "message passing de quantization with applications to compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "frame permutation quantization"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements"

Question: Please tell me which class the center node 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)