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 "estimating graph parameters from random order streams"):
  Abstract: We develop a new algorithmic technique that allows to transfer some constant time approximation algorithms for general graphs into random order streaming algorithms. We illustrate our technique by proving that in random order streams with probability at least $2/3$, #R##N#$\bullet$ the number of connected components of $G$ can be approximated up to an additive error of $\varepsilon n$ using $(\frac{1}{\varepsilon})^{O(1/\varepsilon^3)}$ space, #R##N#$\bullet$ the weight of a minimum spanning tree of a connected input graph with integer edges weights from $\{1,\dots,W\}$ can be approximated within a multiplicative factor of $1+\varepsilon$ using $\big(\frac{1}{\varepsilon}\big)^{\tilde O(W^3/\varepsilon^3)}$ space, #R##N#$\bullet$ the size of a maximum independent set in planar graphs can be approximated within a multiplicative factor of $1+\varepsilon$ using space $2^{(1/\varepsilon)^{(1/\varepsilon)^{\log^{O(1)} (1/\varepsilon)}}}$.

Includes members:
  Member "testable bounded degree graph properties are random order streamable":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "the sparse awakens streaming algorithms for matching size estimation in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "dynamic graph stream algorithms in o n space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "testable properties in general graphs and random order streaming")
  Member "a note on logarithmic space stream algorithms for matchings in low arboricity graphs":
    also connected through other hyperedges:
      (none)
  Member "sublinear estimation of weighted matchings in dynamic data streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "a quasi polynomial time partition oracle for graphs with an excluded minor":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local computation algorithms for spanners")
  Member "a near optimal sublinear time algorithm for approximating the minimum vertex cover size":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing")

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)