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: "parameterized query complexity of hitting set using stability of sunflowers"
  Abstract: In this paper, we study the query complexity of parameterized decision and optimization versions of {\sc Hitting-Set}, {\sc Vertex Cover}, {\sc Packing} , \match{} and {\sc Max-Cut}. The main focus is the query complexity of {\sc Hitting Set}. In doing so, we use an oracle known as \bis{} introduced by Beame et al.~\cite{BeameHRRS18} and its generalizations to hypergraphs. The query models considered are the \gpis{} and \gpise{} oracles : #R##N#(i) the \gpis{} oracle takes as input $d$ pairwise disjoint non-empty vertex subsets $A_1, \ldots, A_d$ in a hypergraph $\cal H$ and answers whether there is a hyperedge with vertices in each $A_i$, #R##N#(ii) the \gpise{} oracle takes the same input and returns a hyperedge that has vertices in each $A_i$; NULL, otherwise. #R##N#The \gpis{} and \gpise{} oracles are used for the decision and optimization versions of the problems, respectively. For $d=2$, we refer \gpis{} and \gpise{} as \bis{} and \bise{}, respectively. We use color coding and queries to the oracles to generate subsamples from the hypergraph, that retain some structural properties of the original hypergraph. We use the stability of the sunflowers in a non-trivial way to do so.

Connected through hyperedge HE-1 (induced by source paper "hyperedge estimation using polylogarithmic subset queries"):
  with members:
    - "fine grained reductions from approximate counting to decision"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "computing exact minimum cuts without knowing the graph"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "finding cycles and trees in sublinear time"

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)