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 "provable and practical approximations for the degree distribution using sublinear graph samples"):
  Abstract: The degree distribution is one of the most fundamental properties used in the analysis of massive graphs. There is a large literature on \emph{graph sampling}, where the goal is to estimate properties (especially the degree distribution) of a large graph through a small, random sample. The degree distribution estimation poses a significant challenge, due to its heavy-tailed nature and the large variance in degrees. #R##N#We design a new algorithm, SADDLES, for this problem, using recent mathematical techniques from the field of \emph{sublinear algorithms}. The SADDLES algorithm gives provably accurate outputs for all values of the degree distribution. For the analysis, we define two fatness measures of the degree distribution, called the \emph{$h$-index} and the \emph{$z$-index}. We prove that SADDLES is sublinear in the graph size when these indices are large. A corollary of this result is a provably sublinear algorithm for any degree distribution bounded below by a power law. #R##N#We deploy our new algorithm on a variety of real datasets and demonstrate its excellent empirical behavior. In all instances, we get extremely accurate approximations for all values in the degree distribution by observing at most $1\%$ of the vertices. This is a major improvement over the state-of-the-art sampling algorithms, which typically sample more than $10\%$ of the vertices to give comparable results. We also observe that the $h$ and $z$-indices of real graphs are large, validating our theoretical analysis.

Includes members:
  Member "measuring and modeling bipartite graphs with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "sublinear time estimation of degree distribution moments the degeneracy connection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints")
  Member "maxoutprobe an algorithm for increasing the size of partially observed networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the limitations of network online learning")
  Member "approximately counting triangles in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "catching the head tail and everything in between a streaming algorithm for the degree distribution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal network sampling")
  Member "the power of an example hidden set size approximation using group queries and conditional sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal randomized group testing algorithm to determine the number of defectives")
  Member "graph sample and hold a framework for big graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph similarity learning a survey")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding the limitations of network online learning")

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)