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: "embedding metrics into ultrametrics and graphs into spanning trees with constant average distortion"
  Abstract: This paper addresses the basic question of how well can a tree approximate distances of a metric space or a graph. Given a graph, the problem of constructing a spanning tree in a graph which strongly preserves distances in the graph is a fundamental problem in network design. We present scaling distortion embeddings where the distortion scales as a function of $\epsilon$, with the guarantee that for each $\epsilon$ the distortion of a fraction $1-\epsilon$ of all pairs is bounded accordingly. Such a bound implies, in particular, that the \emph{average distortion} and $\ell_q$-distortions are small. Specifically, our embeddings have \emph{constant} average distortion and $O(\sqrt{\log n})$ $\ell_2$-distortion. This follows from the following results: we prove that any metric space embeds into an ultrametric with scaling distortion $O(\sqrt{1/\epsilon})$. For the graph setting we prove that any weighted graph contains a spanning tree with scaling distortion $O(\sqrt{1/\epsilon})$. These bounds are tight even for embedding in arbitrary trees. #R##N#For probabilistic embedding into spanning trees we prove a scaling distortion of $\tilde{O}(\log^2 (1/\epsilon))$, which implies \emph{constant} $\ell_q$-distortion for every fixed $q<\infty$.

Connected through hyperedge HE-1 (induced by source paper "covering metric spaces by few trees"):
  with members:
    - "light spanners for high dimensional norms via stochastic decompositions"
    - "ramsey spanning trees and their applications"
    - "a new efficient construction on probabilistic tree embeddings"
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "a constructive proof of the general lovasz local lemma"
    - "ramsey partitions and proximity data structures"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "measured descent a new embedding method for finite metrics"

Connected through hyperedge HE-2 (induced by source paper "an improved algorithm for computing all the best swap edges of a tree spanner"):
  with members:
    - "effective edge fault tolerant single source spanners via best or good swap edges"
    - "sensitivity analysis of minimum spanning trees in sub inverse ackermann time"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-3 (induced by source paper "on notions of distortion and an almost minimum spanning tree with constant average distortion"):
  with members:
    - "constructing light spanners deterministically in near linear time"
    - "the greedy spanner is existentially optimal"
    - "prioritized metric structures and embedding"
    - "nearly tight low stretch spanning trees"
    - "lower stretch spanning trees"

Connected through hyperedge HE-4 (induced by source paper "prioritized metric structures and embedding"):
  with members:
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "approximate distance oracle with constant query time"
    - "approximate distance oracles with improved query time"
    - "a polylogarithmic competitive algorithm for the k server problem"
    - "ramsey partitions and proximity data structures"

Connected through hyperedge HE-5 (induced by source paper "average stretch factor how low does it go"):
  with members:
    - "robust geometric spanners"

Connected through hyperedge HE-6 (induced by source paper "approximating metrics by tree metrics of small distance weighted average stretch"):
  with members:
    - "lower stretch spanning trees"

Connected through hyperedge HE-7 (induced by source paper "lower stretch spanning trees"):
  with members:
    - "solving elliptic finite element systems in near linear time with support preconditioners"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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.DM(Discrete Mathematics)