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 "pruning based distance sketches with provable guarantees on random graphs"):
  Abstract: Measuring the distances between vertices on graphs is one of the most fundamental components in network analysis. Since finding shortest paths requires traversing the graph, it is challenging to obtain distance information on large graphs very quickly. In this work, we present a preprocessing algorithm that is able to create landmark based distance sketches efficiently, with strong theoretical guarantees. When evaluated on a diverse set of social and information networks, our algorithm significantly improves over existing approaches by reducing the number of landmarks stored, preprocessing time, or stretch of the estimated distances. #R##N#On Erdos-Renyi graphs and random power law graphs with degree distribution exponent $2 < \beta < 3$, our algorithm outputs an exact distance data structure with space between $\Theta(n^{5/4})$ and $\Theta(n^{3/2})$ depending on the value of $\beta$, where $n$ is the number of vertices. We complement the algorithm with tight lower bounds for Erdos-Renyi graphs and the case when $\beta$ is close to two.

Includes members:
  Member "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameterized complexity of diameter")
  Member "algorithmic and hardness results for the hub labeling problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting hopsets improved distance oracles for graphs of constant highway dimension and beyond")
  Member "on the complexity of hub labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distance labelings on random power law graphs")
  Member "sublinear distance labeling for sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "popular conjectures as a barrier for dynamic planar graph algorithms")
  Member "route planning in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "hop doubling label indexing for point to point distance querying on scale free networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage")
  Member "a scalable generative graph model with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances in scalable network generation")
  Member "separating hierarchical and general hub labelings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the complexity of hub labeling")

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.SI(Social and Information Networks)