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 "space efficient fast and exact routing in time dependent road networks"):
  Abstract: We study the problem of computing shortest paths in massive road networks with traffic predictions. Existing techniques follow a two phase approach: In an offline, preprocessing step, a database index is built. The index only depends on the road network and the traffic patterns. The path start and end are the input of the query phase, in which shortest-paths are computed. All existing techniques have a large index size, slow query running times, or may compute suboptimal paths. In this work, we introduce CATCHUp (Customizable Approximated Time-dependent Contraction Hierarchies through Unpacking), the first algorithm that simultaneously achieves all three objectives. We perform an extensive experimental study on a set of real world instances and compare our approach with state-of-the-art techniques. Our approach achieves up to 30 times smaller indexes than competing approaches. Additionally, our index can be updated within a few minutes if traffic patterns change.

Includes members:
  Member "faster and better nested dissection orders for customizable contraction hierarchies":
    also connected through other hyperedges:
      (none)
  Member "improved oracles for time dependent road networks":
    also connected through other hyperedges:
      (none)
  Member "engineering oracles for time dependent road networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic time dependent route planning in road networks with user preferences")
  Member "dynamic time dependent route planning in road networks with user preferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shortest feasible paths with charging stops for battery electric vehicles")
  Member "graph bisection with pareto optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "load balanced bottleneck objectives in process mapping")
  Member "route planning in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "customizable contraction hierarchies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)