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 "hyperedge estimation using polylogarithmic subset queries"):
  Abstract: A hypergraph ${\cal H}$ is a \emph{set system} $(U({\cal H}),{\cal F}(H))$, where $U({\cal H})$ denotes the set of $n$ vertices and ${\cal F}(H)$, a set of subsets of $U({\cal H})$, denotes the set of hyperedges. A hypergraph ${\cal H}$ is said to be $d$-uniform if every hyperedge in ${\cal H}$ consists of exactly $d$ vertices. The cardinality of the hyperedge set is denoted as $\ |{{\cal F}({\cal H})}\ |=m({\cal H})$. #R##N#We consider an oracle access to the hypergraph ${\cal H}$ of the following form. Given $d$ (non-empty) pairwise disjoint subsets of vertices $A_1,\ldots,A_d \subseteq U({\cal H})$ of hypergraph ${\cal H}$, the oracle, known as the {\bf Generalized $d$-partite independent set oracle (\gpis)}~(that was introduced in \cite{BishnuGKM018}), answers {\sc Yes} if and only if there exists a hyperedge in ${\cal H}$ having (exactly) one vertex in each $A_i, i \in [d]$. The \gpis{} oracle belongs to the class of oracles for subset queries. The study of subset queries was initiated by Stockmeyer \cite{Stockmeyer85}, and later the model was formalized by Ron and Tsur \cite{RonT16}. Subset queries generalize the set membership queries. #R##N#In this work we give an algorithm for the \hest problem using the \gpis query oracle to obtain an estimate $\widehat{m}$ for $m({\cal H})$ satisfying $(1 - \epsilon) \cdot m({\cal H}) \leq \widehat{m} \leq (1 + \epsilon) \cdot m({\cal H})$. The number of queries made by our algorithm, assuming $d$ as a constant, is polylogarithmic in the number of vertices of the hypergraph. Our work can be seen as a natural generalization of {\sc Edge Estimation} using {\sc Bipartite Independent Set}({\sc BIS}) oracle \cite{BeameHRRS18} and {\sc Triangle Estimation} using {\sc Tripartite Independent Set}({\sc TIS}) oracle \cite{Bhatta-abs-1808-00691}.

Includes members:
  Member "parameterized query complexity of hitting set using stability of sunflowers":
    also connected through other hyperedges:
      (none)
  Member "fine grained reductions from approximate counting to decision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the fine grained complexity of parity problems")
  Member "edge estimation with independent set oracles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximately counting and sampling small witnesses using a colourful decision oracle")
  Member "on approximating the number of k cliques in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six")
  Member "computing exact minimum cuts without knowing the graph":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "approximately counting triangles in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parallel algorithms for small subgraph counting")
  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 "finding cycles and trees in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "walking randomly massively and efficiently")

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)