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: "a note on logarithmic space stream algorithms for matchings in low arboricity graphs"
  Abstract: We present a data stream algorithm for estimating the size of the maximum matching of a low arboricity graph. Recall that a graph has arboricity $\alpha$ if its edges can be partitioned into at most $\alpha$ forests and that a planar graph has arboricity $\alpha=3$. Estimating the size of the maximum matching in such graphs has been a focus of recent data stream research. #R##N#A surprising result on this problem was recently proved by Cormode et al. They designed an ingenious algorithm that returned a $(22.5\alpha+6)(1+\epsilon)$ approximation using a single pass over the edges of the graph (ordered arbitrarily) and $O(\epsilon^{-2}\alpha \cdot \log n \cdot \log_{1+\epsilon} n)$ space. In this note, we improve the approximation factor to $(\alpha+2)(1+\epsilon)$ via a tighter analysis and show that, with a modification of their algorithm, the space required can be reduced to $O(\epsilon^{-2} \log n)$.

Connected through hyperedge HE-1 (induced by source paper "estimating graph parameters from random order streams"):
  with members:
    - "testable bounded degree graph properties are random order streamable"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "dynamic graph stream algorithms in o n space"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

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.DS(Data Structures and Algorithms)