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: "optimal terminal dimensionality reduction in euclidean space"
  Abstract: Let $\varepsilon\in(0,1)$ and $X\subset\mathbb R^d$ be arbitrary with $|X|$ having size $n>1$. The Johnson-Lindenstrauss lemma states there exists $f:X\rightarrow\mathbb R^m$ with $m = O(\varepsilon^{-2}\log n)$ such that $$ \forall x\in X\ \forall y\in X, \|x-y\|_2 \le \|f(x)-f(y)\|_2 \le (1+\varepsilon)\|x-y\|_2 . $$ We show that a strictly stronger version of this statement holds, answering one of the main open questions of [MMMR18]: "$\forall y\in X$" in the above statement may be replaced with "$\forall y\in\mathbb R^d$", so that $f$ not only preserves distances within $X$, but also distances to $X$ from the rest of space. Previously this stronger version was only known with the worse bound $m = O(\varepsilon^{-4}\log n)$. Our proof is via a tighter analysis of (a specific instantiation of) the embedding recipe of [MMMR18].

Connected through hyperedge HE-1 (induced by source paper "labelings vs embeddings on distributed representations of distances"):
  with members:
    - "lossless prioritized embeddings"
    - "almost optimal distance oracles for planar graphs"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "metric embedding via shortest path decompositions"
    - "ramsey spanning trees and their applications"
    - "on notions of distortion and an almost minimum spanning tree with constant average distortion"
    - "prioritized metric structures and embedding"
    - "distance labeling schemes for trees"

Connected through hyperedge HE-2 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "improved coresets for kernel density estimates"
    - "coresets for kernel regression"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "estimating continuous distributions in bayesian classifiers"
    - "union of random minkowski sums and network vulnerability analysis"
    - "geometric inference on kernel density estimates"

Connected through hyperedge HE-3 (induced by source paper "lossless prioritized embeddings"):
  with members:
    - "near isometric terminal embeddings for doubling metrics"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "metric embedding via shortest path decompositions"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "on notions of distortion and an almost minimum spanning tree with constant average distortion"
    - "prioritized metric structures and embedding"
    - "the 4 3 additive spanner exponent is tight"
    - "lower stretch spanning trees"

Connected through hyperedge HE-4 (induced by source paper "approximate nearest neighbor for curves simple efficient and deterministic"):
  with members:
    - "sublinear data structures for short fr echet queries"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "locality sensitive hashing of curves"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-5 (induced by source paper "nonlinear dimension reduction via outer bi lipschitz extensions"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "approximate near neighbors for general symmetric norms"
    - "prioritized metric structures and embedding"
    - "sketching and embedding are equivalent for norms"
    - "constant factor approximation for balanced cut in the pie model"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"
    - "ramsey type theorems for metric spaces with applications to online problems"

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.DS(Data Structures and Algorithms)