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: "fast generation of complex networks with underlying hyperbolic geometry"
  Abstract: Complex networks have become increasingly popular for modeling various real-world phenomena. Realistic generative network models are important in this context as they avoid privacy concerns of real data and simplify complex network research regarding data sharing, reproducibility, and scalability studies. \emph{Random hyperbolic graphs} are a well-analyzed family of geometric graphs. Previous work provided empirical and theoretical evidence that this generative graph model creates networks with non-vanishing clustering and other realistic features. However, the investigated networks in previous applied work were small, possibly due to the quadratic running time of a previous generator. #R##N#In this work we provide the first generation algorithm for these networks with subquadratic running time. We prove a time complexity of $O((n^{3/2}+m) \log n)$ with high probability for the generation process. This running time is confirmed by experimental data with our implementation. The acceleration stems primarily from the reduction of pairwise distance computations through a polar quadtree, which we adapt to hyperbolic space for this purpose. In practice we improve the running time of a previous implementation by at least two orders of magnitude this way. Networks with billions of edges can now be generated in a few minutes. #R##N#Finally, we evaluate the largest networks of this model published so far. Our empirical analysis shows that important features are retained over different graph densities and degree distributions.

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"
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "geometric inhomogeneous random graphs"):
  with members:
    - "average distance in a general class of scale free networks with underlying geometry"
    - "greedy routing and the algorithmic small world phenomenom"
    - "network mapping by replaying hyperbolic growth"
    - "fast generation of dynamic complex networks with underlying hyperbolic geometry"
    - "on the diameter of hyperbolic random graphs"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-3 (induced by source paper "fully dynamic approximation of betweenness centrality"):
  with members:
    - "networkit an interactive tool suite for high performance network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"
    - "betweenness centrality incremental and faster"

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)