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 "sling a near optimal index structure for simrank"):
  Abstract: SimRank is a similarity measure for graph nodes that has numerous applications in practice. Scalable SimRank computation has been the subject of extensive research for more than a decade, and yet, none of the existing solutions can efficiently derive SimRank scores on large graphs with provable accuracy guarantees. In particular, the state-of-the-art solution requires up to a few seconds to compute a SimRank score in million-node graphs, and does not offer any worst-case assurance in terms of the query error. #R##N#This paper presents SLING, an efficient index structure for SimRank computation. SLING guarantees that each SimRank score returned has at most $\varepsilon$ additive error, and it answers any single-pair and single-source SimRank queries in $O(1/\varepsilon)$ and $O(n/\varepsilon)$ time, respectively. These time complexities are near-optimal, and are significantly better than the asymptotic bounds of the most recent approach. Furthermore, SLING requires only $O(n/\varepsilon)$ space (which is also near-optimal in an asymptotic sense) and $O(m/\varepsilon + n\log \frac{n}{\delta}/\varepsilon^2)$ pre-computation time, where $\delta$ is the failure probability of the preprocessing algorithm. We experimentally evaluate SLING with a variety of real-world graphs with up to several millions of nodes. Our results demonstrate that SLING is up to $10000$ times (resp. $110$ times) faster than competing methods for single-pair (resp. single-source) SimRank queries, at the cost of higher space overheads.

Includes members:
  Member "efficient simrank computation via linearization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "realtime index free single source simrank processing on web scale graphs")
  Member "axiomatic ranking of network role similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "realtime index free single source simrank processing on web scale graphs")

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.DB(Databases)