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 "local correlation clustering"):
  Abstract: Correlation clustering is perhaps 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. Despite its theoretical appeal, the practical relevance of correlation clustering still remains largely unexplored, mainly due to the fact that correlation clustering requires the $\Theta(n^2)$ pairwise similarities as input. #R##N#In this paper we initiate the investigation into \emph{local} algorithms for correlation clustering. In \emph{local correlation clustering} we are given the identifier of a single object and we want to return the cluster to which it belongs in some globally consistent near-optimal clustering, using a small number of similarity queries. Local algorithms for correlation clustering open the door to \emph{sublinear-time} algorithms, which are particularly useful when the similarity between items is costly to compute, as it is often the case in many practical application domains. They also imply $(i)$ distributed and streaming clustering algorithms, $(ii)$ constant-time estimators and testers for cluster edit distance, and $(iii)$ property-preserving parallel reconstruction algorithms for clusterability. #R##N#Specifically, we devise a local clustering algorithm attaining a $(3, \varepsilon)$-approximation in time $O(1/\varepsilon^2)$ independently of the dataset size. An explicit approximate clustering for all objects can be produced in time $O(n/\varepsilon)$ (which is provably optimal). We also provide a fully additive $(1,\varepsilon)$-approximation with local query complexity $poly(1/\varepsilon)$ and time complexity $2^{poly(1/\varepsilon)}$. The latter yields the fastest polynomial-time approximation scheme for correlation clustering known to date.

Includes members:
  Member "active learning using smooth relative regret approximations with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting perceptron efficient and label optimal learning of halfspaces")
  Member "fast local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local access to huge random objects through partial sampling")
  Member "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "linear time approximation schemes for the gale berlekamp game and related minimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs")

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)