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 "coresets for clustering with fairness constraints"):
  Abstract: In a recent work, Chierichetti et al. studied the following "fair" variants of classical clustering problems such as $k$-means and $k$-median: given a set of $n$ data points in $\mathbb{R}^d$ and a binary type associated to each data point, the goal is to cluster the points while ensuring that the proportion of each type in each cluster is roughly the same as its underlying proportion. Subsequent work has focused on either extending this setting to when each data point has multiple, non-disjoint sensitive types such as race and gender, or to address the problem that the clustering algorithms in the above work do not scale well. The main contribution of this paper is an approach to clustering with fairness constraints that involve multiple, non-disjoint types, that is {\em also scalable}. Our approach is based on novel constructions of coresets: for the $k$-median objective, we construct an $\varepsilon$-coreset of size $O(\Gamma k^2 \varepsilon^{-d})$ where $\Gamma$ is the number of distinct collections of groups that a point may belong to, and for the $k$-means objective, we show how to construct an $\varepsilon$-coreset of size $O(\Gamma k^3\varepsilon^{-d-1})$. The former result is the first known coreset construction for the fair clustering problem with the $k$-median objective, and the latter result removes the dependence on the size of the full dataset as in Schmidt et al. and generalizes it to multiple, non-disjoint types. Plugging our coresets into existing algorithms for fair clustering such as Backurs et al. results in the fastest algorithms for several cases. Empirically, we assess our approach over the Adult and Bank dataset, and show that the coreset sizes are much smaller than the full dataset; applying coresets indeed accelerates the running time of computing the fair clustering objective while ensuring that the resulting objective difference is small.

Includes members:
  Member "toward controlling discrimination in online ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interventions for ranking in the presence of implicit bias")
  Member "principal fairness removing bias via projections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fairness in clustering with multiple sensitive attributes")
  Member "scalable fair clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "clustering without over representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair correlation clustering")
  Member "on the cost of essentially fair clusterings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fair correlation clustering")
  Member "privacy preserving clustering with constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fair correlation clustering")
  Member "fair and diverse dpp based data summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interventions for ranking in the presence of implicit bias")
  Member "turning big data into tiny data constant size coresets for k means pca and projective clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fair clustering with multiple colors")

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)