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 "approximate correlation clustering using same cluster queries"):
  Abstract: Ashtiani et al. (NIPS 2016) introduced a semi-supervised framework for clustering (SSAC) where a learner is allowed to make same-cluster queries. More specifically, in their model, there is a query oracle that answers queries of the form given any two vertices, do they belong to the same optimal cluster?. Ashtiani et al. showed the usefulness of such a query framework by giving a polynomial time algorithm for the k-means clustering problem where the input dataset satisfies some separation condition. Ailon et al. extended the above work to the approximation setting by giving an efficient (1+\eps)-approximation algorithm for k-means for any small \eps > 0 and any dataset within the SSAC framework. In this work, we extend this line of study to the correlation clustering problem. Correlation clustering is a graph clustering problem where pairwise similarity (or dissimilarity) information is given for every pair of vertices and the objective is to partition the vertices into clusters that minimise the disagreement (or maximises agreement) with the pairwise information given as input. These problems are popularly known as MinDisAgree and MaxAgree problems, and MinDisAgree[k] and MaxAgree[k] are versions of these problems where the number of optimal clusters is at most k. There exist Polynomial Time Approximation Schemes (PTAS) for MinDisAgree[k] and MaxAgree[k] where the approximation guarantee is (1+\eps) for any small \eps and the running time is polynomial in the input parameters but exponential in k and 1/\eps. We obtain an (1+\eps)-approximation algorithm for any small \eps with running time that is polynomial in the input parameters and also in k and 1/\eps. We also give non-trivial upper and lower bounds on the number of same-cluster queries, the lower bound being based on the Exponential Time Hypothesis (ETH).

Includes members:
  Member "approximate clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs")
  Member "almost polynomial ratio eth hardness of approximating densest k subgraph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping")
  Member "clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainable k means and k medians clustering")
  Member "predicting signed edges with o n 1 o 1 log n queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting positive and negative links with noisy queries theory practice")
  Member "efficient clustering with limited distance information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable and robust community detection with randomized sketching")
  Member "correlation clustering with noisy partial information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs")
  Member "local algorithms for interactive clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diameter based interactive structure discovery")
  Member "clustering under perturbation resilience":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "analysis of ward s method")

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)