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 "scalable graph algorithms"):
  Abstract: Processing large complex networks recently attracted considerable interest. Complex graphs are useful in a wide range of applications from technological networks to biological systems like the human brain. Sometimes these networks are composed of billions of entities that give rise to emerging properties and structures. Analyzing these structures aids us in gaining new insights about our surroundings. As huge networks become abundant, there is a need for scalable algorithms to perform analysis. A prominent example is the PageRank algorithm, which is one of the measures used by web search engines such as Google to rank web pages displayed to the user. In order to find these patterns, massive amounts of data have to be acquired and processed. Designing and evaluating scalable graph algorithms to handle these data sets is a crucial task on the road to understanding the underlying systems. #R##N#This habilitation thesis is a summary a broad spectrum of scalable graph algorithms that I developed over the last six years with many coauthors. In general, this research is based on four pillars: multilevel algorithms, practical kernelization, parallelization and memetic algorithms that are highly interconnected. Experiments conducted indicate that our algorithms find better solutions and/or are much more scalable than the previous state-of-the-art.

Includes members:
  Member "exactly solving the maximum weight independent set problem on large real world graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice")
  Member "memetic graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "practical minimum cut algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding all global minimum cuts in practice")
  Member "scalable edge partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "shared memory exact minimum cuts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding all global minimum cuts in practice")
  Member "scalable kernelization for maximum independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding all global minimum cuts in practice")
  Member "graph partitioning with acyclicity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "memetic multilevel hypergraph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilevel acyclic hypergraph partitioning")

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)