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 "lossless prioritized embeddings"):
  Abstract: Given metric spaces $(X,d)$ and $(Y,\rho)$ and an ordering $x_1,x_2,\ldots,x_n$ of $(X,d)$, an embedding $f: X \rightarrow Y$ is said to have a prioritized distortion $\alpha(\cdot)$, if for any pair $x_j,x'$ of distinct points in $X$, the distortion provided by $f$ for this pair is at most $\alpha(j)$. If $Y$ is a normed space, the embedding is said to have prioritized dimension $\beta(\cdot)$, if $f(x_j)$ may have nonzero entries only in its first $\beta(j)$ coordinates. #R##N#The notion of prioritized embedding was introduced by \cite{EFN15}, where a general methodology for constructing such embeddings was developed. Though this methodology enables \cite{EFN15} to come up with many prioritized embeddings, it typically incurs some loss in the distortion. This loss is problematic for isometric embeddings. It is also troublesome for Matousek's embedding of general metrics into $\ell_\infty$, which for a parameter $k = 1,2,\ldots$, provides distortion $2k-1$ and dimension $O(k \log n \cdot n^{1/k})$. #R##N#In this paper we devise two lossless prioritized embeddings. The first one is an isometric prioritized embedding of tree metrics into $\ell_\infty$ with dimension $O(\log j)$. The second one is a prioritized Matousek's embedding of general metrics into $\ell_\infty$, which provides prioritized distortion $2 \lceil k {{\log j} \over {\log n}} \rceil - 1$ and dimension $O(k \log n \cdot n^{1/k})$, again matching the worst-case guarantee $2k-1$ in the distortion of the classical Matousek's embedding. We also provide a dimension-prioritized variant of Matousek's embedding. Finally, we devise prioritized embeddings of general metrics into (single) ultra-metric and of general graphs into (single) spanning tree with asymptotically optimal distortion.

Includes members:
  Member "near isometric terminal embeddings for doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scattering and sparse partitions and their applications")
  Member "nonlinear dimension reduction via outer bi lipschitz extensions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "labelings vs embeddings on distributed representations of distances")
  Member "optimal terminal dimensionality reduction in euclidean space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "labelings vs embeddings on distributed representations of distances")
  Member "metric embedding via shortest path decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scattering and sparse partitions and their applications")
  Member "efficient algorithms for constructing very sparse spanners and emulators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "on notions of distortion and an almost minimum spanning tree with constant average distortion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scattering and sparse partitions and their applications")
  Member "prioritized metric structures and embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scattering and sparse partitions and their applications")
  Member "the 4 3 additive spanner exponent is tight":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weighted additive spanners")

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.DS(Data Structures and Algorithms)