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: "algorithmic and hardness results for the hub labeling problem"
  Abstract: There has been significant success in designing highly efficient algorithms for distance and shortest-path queries in recent years; many of the state-of-the-art algorithms use the hub labeling framework. In this paper, we study the approximability of the Hub Labeling problem. We prove a hardness of $\Omega(\log n)$ for Hub Labeling, matching known approximation guarantees. The hardness result applies to graphs that have multiple shortest paths between some pairs of vertices. No hardness of approximation results were known previously. #R##N#Then, we focus on graphs that have a unique shortest path between each pair of vertices. This is a very natural family of graphs, and much research on the Hub Labeling problem has studied such graphs. We give an $O(\log D)$ approximation algorithm for graphs of diameter $D$ with unique shortest paths. In particular, we get an $O(\log \log n)$ approximation for graphs of polylogarithmic diameter, while previously known algorithms gave an $O(\log n)$ proximation. Finally, we present a polynomial-time approximation scheme (PTAS) and quasi-polynomial time algorithms for Hub Labeling on trees; additionally, we analyze a simple combinatorial heuristic for Hub Labeling on trees, proposed by Peleg in 2000. We show that this heuristic gives an approximation factor of 2.

Connected through hyperedge HE-1 (induced by source paper "exploiting hopsets improved distance oracles for graphs of constant highway dimension and beyond"):
  with members:
    - "linear size hopsets with small hopbound and distributed routing with low memory"
    - "fast and compact exact distance oracle for planar graphs"
    - "better tradeoffs for exact distance oracles in planar graphs"
    - "beyond highway dimension small distance labels using tree skeletons"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "sublinear space distance labeling using hubs"
    - "sublinear distance labeling"

Connected through hyperedge HE-2 (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"
    - "on the complexity of hub labeling"
    - "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-3 (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"
    - "on the complexity of hub labeling"
    - "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"

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)