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: "contraction of timetable networks with realistic transfers"
  Abstract: We successfully contract timetable networks with realistic transfer times. Contraction gradually removes nodes from the graph and adds shortcuts to preserve shortest paths. This reduces query times to 1 ms with preprocessing times around 6 minutes on all tested instances. We achieve this by an improved contraction algorithm and by using a station graph model. Every node in our graph has a one-to-one correspondence to a station and every edge has an assigned collection of connections. Our graph model does not need parallel edges. The query algorithm does not compute a single earliest arrival time at a station but a set of arriving connections that allow best transfer opportunities.

Connected through hyperedge HE-1 (induced by source paper "connection scan algorithm"):
  with members:
    - "trip based public transit routing using condensed search trees"
    - "public transit labeling"
    - "trip based public transit routing"
    - "route planning in transportation networks"

Connected through hyperedge HE-2 (induced by source paper "trip based public transit routing using condensed search trees"):
  with members:
    - "public transit labeling"
    - "trip based public transit routing"

Connected through hyperedge HE-3 (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-4 (induced by source paper "public transit labeling"):
  with members:
    - "route planning in transportation networks"
    - "preach a fast lightweight reachability index using pruning and contraction hierarchies"
    - "simple fast and scalable reachability oracle"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-5 (induced by source paper "trip based public transit routing"):
  with members:
    - "public transit 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)