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: "testing bounded arboricity"
  Abstract: In this paper we consider the problem of testing whether a graph has bounded arboricity. The family of graphs with bounded arboricity includes, among others, bounded-degree graphs, all minor-closed graph classes (e.g. planar graphs, graphs with bounded treewidth) and randomly generated preferential attachment graphs. Graphs with bounded arboricity have been studied extensively in the past, in particular since for many problems they allow for much more efficient algorithms and/or better approximation ratios. #R##N#We present a tolerant tester in the sparse-graphs model. The sparse-graphs model allows access to degree queries and neighbor queries, and the distance is defined with respect to the actual number of edges. More specifically, our algorithm distinguishes between graphs that are $\epsilon$-close to having arboricity $\alpha$ and graphs that $c \cdot \epsilon$-far from having arboricity $3\alpha$, where $c$ is an absolute small constant. The query complexity and running time of the algorithm are $\tilde{O}\left(\frac{n}{\sqrt{m}}\cdot \frac{\log(1/\epsilon)}{\epsilon} + \frac{n\cdot \alpha}{m} \cdot \left(\frac{1}{\epsilon}\right)^{O(\log(1/\epsilon))}\right)$ where $n$ denotes the number of vertices and $m$ denotes the number of edges. In terms of the dependence on $n$ and $m$ this bound is optimal up to poly-logarithmic factors since $\Omega(n/\sqrt{m})$ queries are necessary (and $\alpha = O(\sqrt{m}))$. #R##N#We leave it as an open question whether the dependence on $1/\epsilon$ can be improved from quasi-polynomial to polynomial. Our techniques include an efficient local simulation for approximating the outcome of a global (almost) forest-decomposition algorithm as well as a tailored procedure of edge sampling.

Connected through hyperedge HE-1 (induced by source paper "improved dynamic graph coloring"):
  with members:
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "fully dynamic almost maximal matching breaking the polynomial barrier for worst case time bounds"
    - "the shortest augmenting paths for online matchings on trees"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"

Connected through hyperedge HE-2 (induced by source paper "the arboricity captures the complexity of sampling edges"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "faster sublinear approximations of k cliques for low arboricity graphs"
    - "lower bounds for approximating graph parameters via communication complexity"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximately counting triangles in sublinear time"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-3 (induced by source paper "property testing of planarity in the congest model"):
  with members:
    - "minor excluded network families admit fast distributed algorithms"
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "distributed subgraph detection"
    - "a sublinear tester for outerplanarity and other forbidden minors with one sided error"
    - "distributed detection of cycles"
    - "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"
    - "distributed property testing for subgraph freeness revisited"
    - "on the multiparty communication complexity of testing triangle freeness"

Connected through hyperedge HE-4 (induced by source paper "testing graph clusterability algorithms and lower bounds"):
  with members:
    - "on approximating the number of k cliques in sublinear time"
    - "estimating graph parameters from random order streams"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "testing cluster structure of graphs"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "partitioning into expanders"

Connected through hyperedge HE-5 (induced by source paper "a constructive arboricity approximation scheme"):
  with members:
    - "egalitarian graph orientations"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "linear time algorithms for finding a dominating set of fixed size in degenerated graphs"
    - "logarithmic lower bounds in the cell probe model"
    - "the geometric thickness of low degree graphs"

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)