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: "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.

Connected through hyperedge HE-1 (induced by source paper "realtime index free single source simrank processing on web scale graphs"):
  with members:
    - "prsim sublinear time simrank computation on large power law graphs"
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "efficient simrank computation via linearization"
    - "axiomatic ranking of network role similarity"

Connected through hyperedge HE-2 (induced by source paper "prsim sublinear time simrank computation on large power law graphs"):
  with members:
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "approximate personalized pagerank on dynamic graphs"
    - "panther fast top k similarity search in large networks"
    - "algorithmic complexity of power law networks"
    - "efficient simrank computation via linearization"
    - "axiomatic ranking of network role similarity"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-3 (induced by source paper "probesim scalable single source and top k simrank computations on dynamic graphs"):
  with members:
    - "axiomatic ranking of network role similarity"

Connected through hyperedge HE-4 (induced by source paper "dynamical simrank search on time varying networks"):
  with members:
    - "fast incremental and personalized pagerank"

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.DB(Databases)