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: "fast construction of nets in low dimensional metrics and their applications"
  Abstract: We present a near linear time algorithm for constructing hierarchical nets in finite metric spaces with constant doubling dimension. This data-structure is then applied to obtain improved algorithms for the following problems: approximate nearest neighbor search, well-separated pair decomposition, spanner construction, compact representation scheme, doubling measure, and computation of the (approximate) Lipschitz constant of a function. In all cases, the running (preprocessing) time is near linear and the space being used is linear.

Connected through hyperedge HE-1 (induced by source paper "a data dependent algorithm for querying earth mover s distance with low doubling dimension"):
  with members:
    - "conditional hardness of earth mover distance"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "optimal transport for domain adaptation"
    - "a survey on truth discovery"
    - "on constant factor approximation for earth mover distance over doubling metrics"

Connected through hyperedge HE-2 (induced by source paper "distributed construction of light networks"):
  with members:
    - "light spanners for high dimensional norms via stochastic decompositions"
    - "greedy spanners are optimal in doubling metrics"
    - "distributed exact shortest paths in sublinear time"
    - "improved distributed algorithms for exact shortest paths"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "constructing light spanners deterministically in near linear time"
    - "minor free graphs have light spanners"
    - "the greedy spanner is existentially optimal"

Connected through hyperedge HE-3 (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"
    - "embedding metrics into ultrametrics and graphs into spanning trees with constant average distortion"
    - "measured descent a new embedding method for finite metrics"

Connected through hyperedge HE-4 (induced by source paper "near linear time approximation schemes for steiner tree and forest in low dimensional spaces"):
  with members:
    - "greedy spanners are optimal in doubling metrics"
    - "the greedy spanner is existentially optimal"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "a light metric spanner"
    - "a polynomial time approximation scheme for euclidean steiner forest"
    - "an efficient polynomial time approximation scheme for steiner forest in planar graphs"
    - "approximation schemes for steiner forest on planar graphs and graphs of bounded treewidth"

Connected through hyperedge HE-5 (induced by source paper "a composable coreset for k center in doubling metrics"):
  with members:
    - "greedy strategy works for k center clustering with outliers and coreset construction"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "efficient parallel algorithms for k center clustering"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "randomized composable core sets for distributed submodular maximization"
    - "approximating k median via pseudo approximation"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-6 (induced by source paper "sublinear data structures for short fr echet queries"):
  with members:
    - "the one way communication complexity of dynamic time warping distance"
    - "approximate nearest neighbor for curves simple efficient and deterministic"
    - "on optimal polyline simplification using the hausdorff and fr echet distance"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "coresets and sketches"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

Connected through hyperedge HE-7 (induced by source paper "fully dynamic k center clustering in doubling metrics"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "a tree structure for dynamic facility location"
    - "fully dynamic hierarchical diameter k clustering and k center"
    - "small space stream summary for matroid center"
    - "expander decomposition and pruning faster stronger and simpler"
    - "dynamic clustering to minimize the sum of radii"
    - "the new data and new challenges in multimedia research"

Connected through hyperedge HE-8 (induced by source paper "greedy strategy works for k center clustering with outliers and coreset construction"):
  with members:
    - "a composable coreset for k center in doubling metrics"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "the non uniform k center problem"
    - "clustering is difficult only when it does not matter"

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)