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: "external memory priority queues with decrease key and applications to graph algorithms"
  Abstract: We present priority queues in the external memory model with block size $B$ and main memory size $M$ that support on $N$ elements, operation Update (a combination of operations Insert and Decrease-Key) in $O\left( \frac{1}{B}\log_{\frac{M}{B}} \frac{N}{B}\right)$ amortized I/Os and operations Extract-Min and Delete in $O\left( \lceil \frac{M^{\varepsilon}}{B} \log_{\frac{M}{B}} \frac{N}{B} \rceil \log_{\frac{M}{B}} \frac{N}{B}\right)$ amortized I/Os, for any real $\varepsilon \in \left(0,1\right)$, using $O\left( \frac{N}{B}\log_{\frac{M}{B}} \frac{N}{B}\right)$ blocks. Previous I/O-efficient priority queues either support these operations in $O\left(\frac{1}{B}\log_2 \frac{N}{B}\right)$ amortized I/Os [Kumar and Schwabe, SPDP '96] or support only operations Insert, Delete and Extract-Min in optimal $O\left( \frac{1}{B}\log_{\frac{M}{B}} \frac{N}{B}\right) $ amortized I/Os, however without supporting Decrease-Key [Fadel et al., TCS '99]. #R##N#We also present buffered repository trees that support on a multi-set of $N$ elements, operation Insert in $O\left(\frac{1}{B}\log_{\frac{M}{B}} \frac{N}{B}\right)$ I/Os and operation Extract on $K$ extracted elements in $O\left( M^{\varepsilon}\log_{\frac{M}{B}} \frac{N}{B} + K/B\right)$ amortized I/Os, using $O \left(\frac{N}{B}\right)$ blocks. Previous results achieve $O\left(\frac{1}{B}\log_2 \frac{N}{B}\right)$ I/Os and $O\left( \log_2 \frac{N}{B} + \frac{K}{B}\right)$ I/Os, respectively [Buchsbaum et al., SODA '00]. #R##N#Our results imply improved $O\left(\frac{E}{B}\log_{\frac{M}{B}} \frac{E}{B}\right)$ I/Os for single-source shortest paths, depth-first search and breadth-first search algorithms on massive directed dense graphs $\left(V,E\right)$ with $E = \Omega \left( V^{1+\varepsilon}\right), \varepsilon > 0$ and $V = \Omega \left( M \right)$, which is equal to the I/O-optimal bound for sorting $E$ values in external memory.

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)