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 "near neighbor preserving dimension reduction for doubling subsets of ell_1"):
  Abstract: Randomized dimensionality reduction has been recognized as one of the fundamental techniques in handling high-dimensional data. Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean $(\ell_2)$ metric and, much less, for the Manhattan $(\ell_1)$ metric. #R##N#Our primary motivation is the approximate nearest neighbor problem in $\ell_1$. We exploit its reduction to the decision-with-witness version, called approximate \textit{near} neighbor, which incurs a roughly logarithmic overhead. In 2007, Indyk and Naor, in the context of approximate nearest neighbors, introduced the notion of nearest neighbor-preserving embeddings. These are randomized embeddings between two metric spaces with guaranteed bounded distortion only for the distances between a query point and a point set. Such embeddings are known to exist for both $\ell_2$ and $\ell_1$ metrics, as well as for doubling subsets of $\ell_2$. #R##N#In this paper, we propose a dimension reduction, \textit{near} neighbor-preserving embedding for doubling subsets of $\ell_1$. Our approach is to represent the point set with a carefully chosen covering set, and then apply a random projection to that covering set. We study two cases of covering sets: $c$-approximate $r$-nets and randomly shifted grids, and we discuss the tradeoff between them in terms of preprocessing time and target dimension.

Includes members:
  Member "approximate near neighbors for general symmetric norms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "approximate greedy clustering and distance selection for graph metrics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high dimensional approximate r nets")
  Member "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate nearest neighbor search in high dimensions")
  Member "randomized embeddings with slack and high dimensional approximate nearest neighbor":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practical linear space approximate near neighbors in high dimension")
  Member "fast construction of nets in low dimensional metrics and their applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a data dependent algorithm for querying earth mover s distance with low doubling dimension")

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)