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: "cilkmem algorithms for analyzing the memory high water mark of fork join parallel programs"
  Abstract: Software engineers designing recursive fork-join programs destined to run on massively parallel computing systems must be cognizant of how their program's memory requirements scale in a many-processor execution. Although tools exist for measuring memory usage during one particular execution of a parallel program, such tools cannot bound the worst-case memory usage over all possible parallel executions. #R##N#This paper introduces Cilkmem, a tool that analyzes the execution of a deterministic Cilk program to determine its $p$-processor memory high-water mark (MHWM), which is the worst-case memory usage of the program over \emph{all possible} $p$-processor executions. Cilkmem employs two new algorithms for computing the $p$-processor MHWM. The first algorithm calculates the exact $p$-processor MHWM in $O(T_1 \cdot p)$ time, where $T_1$ is the total work of the program. The second algorithm solves, in $O(T_1)$ time, the approximate threshold problem, which asks, for a given memory threshold $M$, whether the $p$-processor MHWM exceeds $M/2$ or whether it is guaranteed to be less than $M$. Both algorithms are memory efficient, requiring $O(p \cdot D)$ and $O(D)$ space, respectively, where $D$ is the maximum call-stack depth of the program's execution on a single thread. #R##N#Our empirical studies show that Cilkmem generally exhibits low overheads. Across ten application benchmarks from the Cilkbench suite, the exact algorithm incurs a geometric-mean multiplicative overhead of $1.54$ for $p=128$, whereas the approximation-threshold algorithm incurs an overhead of $1.36$ independent of $p$. In addition, we use Cilkmem to reveal and diagnose a previously unknown issue in a large image-alignment program contributing to unexpectedly high memory usage under parallel executions.

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)