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 "updating dynamic random hyperbolic graphs in sublinear time"):
  Abstract: Generative network models play an important role in algorithm development, scaling studies, network analysis, and realistic system benchmarks for graph data sets. A complex network model gaining considerable popularity builds random hyperbolic graphs, generated by distributing points within a disk in the hyperbolic plane and then adding edges between points with a probability depending on their hyperbolic distance. #R##N#We present a dynamic extension to model gradual network change, while preserving at each step the point position probabilities. To process the dynamic changes efficiently, we formalize the concept of a probabilistic neighborhood: Let $P$ be a set of $n$ points in Euclidean or hyperbolic space, $q$ a query point, $\operatorname{dist}$ 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 $f(\operatorname{dist}(p,q))$. 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. This enables us to process a node movement in random hyperbolic graphs in sublinear time, resulting in a speedup of about one order of magnitude in practice compared to the fastest previous approach. Apart from that, our query algorithm is also applicable to Euclidean geometry, making it of independent interest for other sampling or probabilistic spreading scenarios.

Includes members:
  Member "nearest neighbor searching under uncertainty ii":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the expected diameter width and complexity of a stochastic convex hull")
  Member "querying probabilistic neighborhoods in spatial data sets efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating massive complex networks with hyperbolic geometry faster in practice")
  Member "generating massive complex networks with hyperbolic geometry faster in practice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in scalable network generation")
  Member "generating scaled replicas of real world complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiscale planar graph generation")
  Member "sampling geometric inhomogeneous random graphs in linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding the effectiveness of data reduction in public transportation networks")
  Member "geometric inhomogeneous random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances in scalable network generation")
  Member "approximating betweenness centrality in fully dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach")
  Member "on the diameter of hyperbolic random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recent advances in scalable network generation")

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)