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 "exploration of a graph based density sensitive metric"):
  Abstract: We consider a simple graph-based metric on points in Euclidean space known as the edge-squared metric. This metric is defined by squaring the Euclidean distance between points, and taking the shortest paths on the resulting graph. This metric has been studied before in wireless networks and machine learning, and has the density sensitive property: distances between two points in the same cluster are short, even if their Euclidean distance is long. This property is desirable in machine learning. #R##N#In this paper, we show that this metric is equal to previously studied geodesic-based metric defined on points in Euclidean space. Previous best work showed that these metrics are $3$-approximations of each other. It was not known or suspected that these metrics were equal. We give fast algorithms to compute sparse spanners of this distance, a problem that can be seen as a generalization of both the Euclidean spanner and the Euclidean MST problem. Spanners of the edge-squared metric are faster to compute and sparser than the best known Euclidean spanners in a variety of settings.

Includes members:
  Member "strong coresets for k median and subspace approximation goodbye dimension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coresets for clustering in graphs of bounded treewidth")
  Member "adaptive metrics for adaptive samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an efficient algorithm for computing high quality paths amid polygonal obstacles")
  Member "massively parallel algorithms and hardness for single linkage clustering under ell_p distances":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "polynomial representations of threshold functions and algorithmic applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating text to pattern hamming distances")
  Member "approximating nearest neighbor distances":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive metrics for adaptive samples")
  Member "optimal euclidean spanners really short thin and lanky":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph spanners a tutorial review")
  Member "semi supervised learning with density based distances":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "power weighted shortest paths for unsupervised learning")
  Member "efficient regression in metric spaces via approximate lipschitz extension":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "truly optimal euclidean 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.CG(Computational Geometry)