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 "query efficient correlation clustering"):
  Abstract: Correlation clustering is arguably the most natural formulation of clustering. Given n objects and a pairwise similarity measure, the goal is to cluster the objects so that, to the best possible extent, similar objects are put in the same cluster and dissimilar objects are put in different clusters. #R##N#A main drawback of correlation clustering is that it requires as input the $\Theta(n^2)$ pairwise similarities. This is often infeasible to compute or even just to store. In this paper we study \emph{query-efficient} algorithms for correlation clustering. Specifically, we devise a correlation clustering algorithm that, given a budget of $Q$ queries, attains a solution whose expected number of disagreements is at most $3\cdot OPT + O(\frac{n^3}{Q})$, where $OPT$ is the optimal cost for the instance. Its running time is $O(Q)$, and can be easily made non-adaptive (meaning it can specify all its queries at the outset and make them in parallel) with the same guarantees. Up to constant factors, our algorithm yields a provably optimal trade-off between the number of queries $Q$ and the worst-case error attained, even for adaptive algorithms. #R##N#Finally, we perform an experimental study of our proposed method on both synthetic and real data, showing the scalability and the accuracy of our algorithm.

Includes members:
  Member "correlation clustering with adaptive similarity queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair correlation clustering")
  Member "correlation clustering with same cluster queries bounded by optimal cost":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "revisiting semi supervised learning with graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral graph attention network")
  Member "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair correlation clustering")
  Member "local correlation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "crowder crowdsourcing entity resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crowdsourced collective entity resolution with relational match propagation")

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)