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 "multilevel acyclic hypergraph partitioning"):
  Abstract: A directed acyclic hypergraph is a generalized concept of a directed acyclic graph, where each hyperedge can contain an arbitrary number of tails and heads. Directed hypergraphs can be used to model data flow and execution dependencies in streaming applications. Thus, hypergraph partitioning algorithms can be used to obtain efficient parallelizations for multiprocessor architectures. However, an acyclicity constraint on the partition is necessary when mapping streaming applications to embedded multiprocessors due to resource restrictions on this type of hardware. The acyclic hypergraph partitioning problem is to partition the hypernodes of a directed acyclic hypergraph into a given number of blocks of roughly equal size such that the corresponding quotient graph is acyclic while minimizing an objective function on the partition. #R##N#Here, we contribute the first n-level algorithm for the acyclic hypergraph partitioning problem. Our focus is on acyclic hypergraphs where hyperedges can have one head and arbitrary many tails. Based on this, we engineer a memetic algorithm to further reduce communication cost, as well as to improve scheduling makespan on embedded multiprocessor architectures. Experiments indicate that our algorithm outperforms previous algorithms that focus on the directed acyclic graph case which have previously been employed in the application domain. Moreover, our experiments indicate that using the directed hypergraph model for this type of application yields a significantly smaller makespan.

Includes members:
  Member "memetic graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable graph algorithms")
  Member "network flow based refinement for multilevel hypergraph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph cuts with general splitting functions")
  Member "scalable edge partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "2ps high quality edge partitioning with two phase streaming")
  Member "graph partitioning with acyclicity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable graph algorithms")
  Member "memetic multilevel hypergraph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable graph algorithms")
  Member "high quality shared memory graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable graph algorithms")
  Member "partitioning trillion edge graphs in minutes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed edge partitioning for trillion edge graphs")
  Member "k way hypergraph partitioning via n level recursive bisection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable graph algorithms")

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)