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: "customizable contraction hierarchies"
  Abstract: We consider the problem of quickly computing shortest paths in weighted graphs given auxiliary data derived in an expensive preprocessing phase. By adding a fast weight-customization phase, we extend Contraction Hierarchies by Geisberger et al to support the three-phase workflow introduced by Delling et al. Our Customizable Contraction Hierarchies use nested dissection orders as suggested by Bauer et al. We provide an in-depth experimental analysis on large road and game maps that clearly shows that Customizable Contraction Hierarchies are a very practicable solution in scenarios where edge weights often change.

Connected through hyperedge HE-1 (induced by source paper "scalable graph algorithms"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "memetic graph clustering"
    - "practical minimum cut algorithms"
    - "scalable edge partitioning"
    - "shared memory exact minimum cuts"
    - "scalable kernelization for maximum independent sets"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"

Connected through hyperedge HE-2 (induced by source paper "space efficient fast and exact routing in time dependent road networks"):
  with members:
    - "faster and better nested dissection orders for customizable contraction hierarchies"
    - "improved oracles for time dependent road networks"
    - "engineering oracles for time dependent road networks"
    - "dynamic time dependent route planning in road networks with user preferences"
    - "graph bisection with pareto optimization"
    - "route planning in transportation networks"

Connected through hyperedge HE-3 (induced by source paper "faster and better nested dissection orders for customizable contraction hierarchies"):
  with members:
    - "advanced multilevel node separator algorithms"
    - "graph bisection with pareto optimization"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"

Connected through hyperedge HE-4 (induced by source paper "evaluation of a flow based hypergraph bipartitioning algorithm"):
  with members:
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "memetic multilevel hypergraph partitioning"
    - "graph bisection with pareto optimization"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-5 (induced by source paper "shortest feasible paths with charging stops for battery electric vehicles"):
  with members:
    - "dynamic time dependent route planning in road networks with user preferences"
    - "route planning in transportation networks"
    - "two phase bicriterion search for finding fast and efficient electric vehicle routes"

Connected through hyperedge HE-6 (induced by source paper "more hierarchy in route planning using edge hierarchies"):
  with members:
    - "route planning in transportation networks"
    - "transit node routing reconsidered"

Connected through hyperedge HE-7 (induced by source paper "a with perfect potentials"):
  with members:
    - "route planning in transportation networks"

Connected through hyperedge HE-8 (induced by source paper "distributed evolutionary k way node separators"):
  with members:
    - "advanced multilevel node separator algorithms"
    - "graph bisection with pareto optimization"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"

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)