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 "all distances sketches revisited hip estimators for massive graphs analysis"):
  Abstract: Graph datasets with billions of edges, such as social and Web graphs, are prevalent, and scalable computation is critical. All-distances sketches (ADS) [Cohen 1997], are a powerful tool for scalable approximation of statistics. #R##N#The sketch is a small size sample of the distance relation of a node which emphasizes closer nodes. Sketches for all nodes are computed using a nearly linear computation and estimators are applied to sketches of nodes to estimate their properties. #R##N#We provide, for the first time, a unified exposition of ADS algorithms and applications. We present the Historic Inverse Probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of statistics. For the important special cases of neighborhood cardinalities (the number of nodes within some query distance) and closeness centralities, HIP estimators have at most half the variance of previous estimators and we show that this is essentially optimal. Moreover, HIP obtains a polynomial improvement for more general statistics and the estimators are simple, flexible, unbiased, and elegant. #R##N#For approximate distinct counting on data streams, HIP outperforms the original estimators for the HyperLogLog MinHash sketches (Flajolet et al. 2007), obtaining significantly improved estimation quality for this state-of-the-art practical algorithm.

Includes members:
  Member "timed influence computation and maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reverse ranking by graph structure model and scalable algorithms")
  Member "computing classic closeness centrality at scale":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning in network centrality measures tutorial and outlook")
  Member "sketch based influence maximization and computation scaling up with guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rcelf a residual based approach for influence maximization problem")
  Member "axioms for centrality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maximizing influence based group shapley centrality")
  Member "fast exact shortest path distance queries on large networks by pruned landmark labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relaxing relationship queries on graph data")
  Member "four degrees of separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diffusion improves graph learning")
  Member "robustness of social networks comparative results based on distance distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing classic closeness centrality at scale")
  Member "hyperanf approximating the neighbourhood function of very large graphs on a budget":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")

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)