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 "querying probabilistic neighborhoods in spatial data sets efficiently"):
  Abstract: The probability that two spatial objects establish some kind of mutual connection often depends on their proximity. To formalize this concept, we define the notion of a probabilistic neighborhood: Let P be a set of n points in \(\mathbb {R}^d\), \(q \in \mathbb {R}^d\) a query point, \({\text {dist}}\) a distance metric, and \(f : \mathbb {R}^+ \rightarrow [0,1]\) a monotonically decreasing function. Then, the probabilistic neighborhood N(q, f) of q with respect to f is a random subset of P and each point \(p \in P\) belongs to N(q, f) with probability \(f({\text {dist}}(p,q))\). Possible applications include query sampling and the simulation of probabilistic spreading phenomena, as well as other scenarios where the probability of a connection between two entities decreases with their distance. We present a fast, sublinear-time query algorithm to sample probabilistic neighborhoods from planar point sets. For certain distributions of planar P, we prove that our algorithm answers a query in \(O((|N(q,f)| + \sqrt{n})\log n)\) time with high probability. In experiments this yields a speedup over pairwise distance probing of at least one order of magnitude, even for rather small data sets with \(n=10^5\) and also for other point distributions not covered by the theoretical results.

Includes members:
  Member "nearest neighbor searching under uncertainty ii":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time")
  Member "geometric inhomogeneous random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances in scalable network generation")
  Member "networkit a tool suite for large scale complex network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")

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)