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: "on the complexity of hub labeling"
  Abstract: Hub Labeling (HL) is a data structure for distance oracles. Hierarchical HL (HHL) is a special type of HL, that received a lot of attention from a practical point of view. However, theoretical questions such as NP-hardness and approximation guarantee for HHL algorithms have been left aside. In this paper we study HL and HHL from the complexity theory point of view. We prove that both HL and HHL are NP-hard, and present upper and lower bounds for the approximation ratios of greedy HHL algorithms used in practice. We also introduce a new variant of the greedy HHL algorithm and a proof that it produces small labels for graphs with small highway dimension.

Connected through hyperedge HE-1 (induced by source paper "pruning based distance sketches with provable guarantees on random graphs"):
  with members:
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "algorithmic and hardness results for the hub labeling problem"
    - "sublinear distance labeling for sparse graphs"
    - "route planning in transportation networks"
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "a scalable generative graph model with community structure"
    - "separating hierarchical and general hub labelings"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-2 (induced by source paper "distance labelings on random power law graphs"):
  with members:
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "algorithmic and hardness results for the hub labeling problem"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "approximate distance oracles with improved preprocessing time"
    - "linear space approximate distance oracles for planar bounded genus and minor free graphs"

Connected through hyperedge HE-3 (induced by source paper "algorithmic and hardness results for the hub labeling problem"):
  with members:
    - "lower bounds in the preprocessing and query phases of routing algorithms"
    - "route planning in transportation networks"
    - "analytical approach to parallel repetition"

Connected through hyperedge HE-4 (induced by source paper "lower bounds in the preprocessing and query phases of routing algorithms"):
  with members:
    - "route planning in transportation networks"
    - "transit node routing reconsidered"

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)