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 "selection on x_1 x_2 cdots x_m with layer ordered heaps"):
  Abstract: Selection on $X_1+X_2+\cdots + X_m$ is an important problem with many applications in areas such as max-convolution, max-product Bayesian inference, calculating most probable isotopes, and computing non-parametric test statistics, among others. Faster-than-naive approaches exist for $m=2$: Johnson \& Mizoguchi (1978) find the smallest $k$ values in $A+B$ with runtime $O(n \log(n))$. Frederickson \& Johnson (1982) created a method for finding the $k$ smallest values in $A+B$ with runtime $O(n + \min(k,n)\log(\frac{k}{\min(k,n)}))$. In 1993, Frederickson published an optimal algorithm for selection on $A+B$, which runs in $O(n+k)$. In 2018, Kaplan \emph{et al.} described another optimal algorithm in terms Chazelle's of soft heaps. No fast methods exist for $m>2$. Johnson \& Mizoguchi (1978) introduced a method to compute the minimal $k$ terms when $m>2$, but that method runs in $O(m\cdot n^{\frac{m}{2}} \log(n))$ and is inefficient when $m \gg 1$. #R##N#In this paper, we introduce the first efficient methods for problems where $m>2$. We introduce the ``layer-ordered heap,'' a simple special class of heap with which we produce a new, fast selection algorithm on the Cartesian product. Using this new algorithm to perform $k$-selection on the Cartesian product of $m$ arrays of length $n$ has runtime $\in o(m\cdot n + k\cdot m)$. We also provide implementations of the algorithms proposed and their performance in practice.

Includes members:
  Member "selection from heaps row sorted matrices and x y using soft heaps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding a mediocre player")
  Member "a fast numerical method for max convolution and the application to efficient max product inference in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating apsp without scaling equivalence of approximate min plus and exact min max")

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)