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: "transit node routing reconsidered"
  Abstract: Transit Node Routing (TNR) is a fast and exact distance oracle for road networks. We show several new results for TNR. First, we give a surprisingly simple implementation fully based on Contraction Hierarchies that speeds up preprocessing by an order of magnitude approaching the time for just finding a CH (which alone has two orders of magnitude larger query time). We also develop a very effective purely graph theoretical locality filter without any compromise in query times. Finally, we show that a specialization to the online many-to-one (or one-to-many) shortest path further speeds up query time by an order of magnitude. This variant even has better query time than the fastest known previous methods which need much more space.

Connected through hyperedge HE-1 (induced by source paper "self driving cars a survey"):
  with members:
    - "traffic light recognition using deep learning and prior maps for autonomous cars"
    - "effortless deep training for traffic sign detection using templates and arbitrary natural images"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "dct maps compact differentiable lidar maps based on the cosine transform"
    - "visual global localization with a hybrid wnn cnn approach"
    - "map memorization and forgetting in the iara autonomous car"
    - "mapping road lanes using laser remission and deep neural networks"
    - "roadtracer automatic extraction of road networks from aerial images"

Connected through hyperedge HE-2 (induced by source paper "more hierarchy in route planning using edge hierarchies"):
  with members:
    - "route planning in transportation networks"
    - "customizable contraction hierarchies"

Connected through hyperedge HE-3 (induced by source paper "a survey of shortest path algorithms"):
  with members:
    - "faster parametric shortest path and minimum balance algorithms"
    - "dynamic approximate all pairs shortest paths breaking the o mn barrier and derandomization"
    - "linear space approximate distance oracles for planar bounded genus and minor free graphs"
    - "randomized speedup of the bellman ford algorithm"
    - "exact distance oracles for planar graphs"
    - "fast paths in large scale dynamic road networks"

Connected through hyperedge HE-4 (induced by source paper "route planning in transportation networks"):
  with members:
    - "trip based public transit routing"
    - "optimal hub labeling is np complete"
    - "salt a unified framework for all shortest path query variants on road networks"
    - "two phase bicriterion search for finding fast and efficient electric vehicle routes"
    - "customizable contraction hierarchies"
    - "shortest path and distance queries on road networks towards bridging theory and practice"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "distance oracles for time dependent networks"

Connected through hyperedge HE-5 (induced by source paper "lower bounds in the preprocessing and query phases of routing algorithms"):
  with members:
    - "on the complexity of hub labeling"
    - "route planning in transportation networks"

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)