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: "simple algorithms for scheduling moldable tasks"
  Abstract: Moldable tasks are a model of parallel processing that allows a choice of the numbers of processors assigned to them, enabling efficient use of modern parallel systems such as massively parallel processing systems and clusters. We study the classical problem of scheduling a set T of n moldable tasks on m processors. This problem has been extensively studied for the objective of minimizing the makespan under the assumption of monotonic tasks, that is for tasks whose workload increases when executed in parallel on multiple processors; so far, the best result of interest is a (3/2+\epsilon)-approximation algorithm with a complexity of O(mnlog(n/\epsilon)). Motivated by recent observations in benchmark studies, we consider the case where the speedup is linear for small parallelization. Specifically, we introduce the following new assumption: the workload of a task is constant when assigned no more than \delta processors (hence its execution time decreases linearly); but when assigned $>\delta$ processors the workload increases and the execution time decreases non-linearly. #R##N#In this paper, for an arbitrary value of \delta, we propose simple and general procedures to separately optimize two scheduling objectives: (1) minimizing the makespan, and (2) maximizing the sum of values of tasks completed by a deadline. In particular, we propose a 1/\theta(\delta)*(1+\epsilon)-approximation algorithm with a complexity of O(nlog(n/\epsilon)) for the first objective, and, a \theta(\delta)-approximation algorithm with a complexity of O(n) for the second objective. For example, \theta(5) and \theta(27) equals 3/4 and 7/8 approximately in the context of this paper. To the best of our knowledge, our work is the first that considers the second objective for moldable tasks, while it has been considered for other types of tasks.

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)