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 "prsim sublinear time simrank computation on large power law graphs"):
  Abstract: SimRank is a classic measure of the similarities of nodes in a graph. Given a node u in graph $G =(V, E)$, a \em single-source SimRank query returns the SimRank similarities $s(u, v)$ between node u and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes n, which renders them inapplicable for real-time and interactive analysis. This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where m is the number of edges in the graph, and guarantees a query time that depends on the \em reverse PageRank distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph. Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.

Includes members:
  Member "probesim scalable single source and top k simrank computations on dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "realtime index free single source simrank processing on web scale graphs")
  Member "sling a near optimal index structure for simrank":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "realtime index free single source simrank processing on web scale graphs")
  Member "personalized pagerank estimation and search a bidirectional approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "approximate personalized pagerank on dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "panther fast top k similarity search in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "diversified top k similarity search in large attributed networks")
  Member "algorithmic complexity of power law networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed maximal independent set on scale free networks")
  Member "efficient simrank computation via linearization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (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.DS(Data Structures and Algorithms)