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 dynamic complex networks with underlying hyperbolic geometry"
  Abstract: Complex networks have become increasingly popular for modeling real-world phenomena, ranging from web hyperlinks to interactions between people. 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. We study a geometric model creating unit-disk graphs in hyperbolic space. Previous work provided empirical and theoretical evidence that this model creates networks with a hierarchical structure and other realistic features. However, the investigated networks were small, possibly due to a quadratic running time of a straightforward implementation. % We provide a faster generator for a representative subset of these networks. Our experiments indicate a time complexity of $O((n^{3/2}+m) \log n)$ for our implementation and thus confirm our theoretical considerations. To our knowledge our implementation is the first one with subquadratic running time. The acceleration stems primarily from the reduction of pairwise distance computations through a polar quadtree newly adapted to hyperbolic space. We also extend the generator to an alternative dynamic model which preserves graph properties in expectation. Finally, we generate and evaluate the largest networks of this model published so far. Our implementation computes networks with billions of edges in less than an hour. A comprehensive network analysis shows that important features of complex networks, such as a low diameter, power-law degree distribution and a high clustering coefficient, are retained over different graph sizes and densities.

Connected through hyperedge HE-1 (induced by source paper "engineering parallel algorithms for community detection in massive networks"):
  with members:
    - "mixing local and global information for community detection in large networks"
    - "networkit an interactive tool suite for high performance network analysis"
    - "parallel graph partitioning for complex networks"
    - "recent advances in graph partitioning"
    - "on the analysis of a label propagation algorithm for community detection"
    - "defining and evaluating network communities based on ground truth"
    - "static and dynamic aspects of scientific collaboration networks"

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"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-3 (induced by source paper "on the diameter of hyperbolic random graphs"):
  with members:
    - "network mapping by replaying hyperbolic growth"
    - "geometric inhomogeneous random graphs"
    - "general drift analysis with tail bounds"
    - "sustaining the internet with hyperbolic mapping"

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)