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: "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.

Connected through hyperedge HE-1 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "generating scaled replicas of real world complex networks"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "geometric inhomogeneous random graphs"
    - "approximating betweenness centrality in fully dynamic networks"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"

Connected through hyperedge HE-2 (induced by source paper "generating massive complex networks with hyperbolic geometry faster in practice"):
  with members:
    - "geometric inhomogeneous random graphs"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-3 (induced by source paper "networkit a tool suite for large scale complex network analysis"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "fully dynamic approximation of betweenness centrality"
    - "approximating betweenness centrality in large evolving networks"
    - "social structure of facebook networks"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

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)