human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"
    - "clustering with fairness constraints a flexible and scalable approach"

Connected through hyperedge HE-2 (induced by source paper "fair clustering with multiple colors"):
  with members:
    - "fair regression quantitative definitions and reduction based algorithms"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "privacy preserving clustering with constraints"
    - "constant factor fpt approximation for capacitated k median"
    - "a unified framework for clustering constrained data without locality property"

Connected through hyperedge HE-3 (induced by source paper "individual fairness for k clustering"):
  with members:
    - "r enyi fair inference"
    - "a survey on bias and fairness in machine learning"
    - "fairness in clustering with multiple sensitive attributes"
    - "a center in your neighborhood fairness in facility location"
    - "scalable fair clustering"
    - "clustering without over representation"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "on the cost of essentially fair clusterings"

Connected through hyperedge HE-4 (induced by source paper "explainable k means and k medians clustering"):
  with members:
    - "individual fairness for k clustering"
    - "same cluster querying for overlapping clusters"
    - "online k means clustering"
    - "fair algorithms for clustering"
    - "unexpected effects of online k means clustering"
    - "sequential no substitution k median clustering"
    - "scalable fair clustering"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"

Connected through hyperedge HE-5 (induced by source paper "interventions for ranking in the presence of implicit bias"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "toward controlling discrimination in online ad auctions"
    - "pairwise fairness for ranking and regression"
    - "fair and diverse dpp based data summarization"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "fairness of exposure in rankings"
    - "multiwinner voting with fairness constraints"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-6 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for ordered weighted clustering"
    - "k means clustering of lines for big data"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"

Question: Please tell me which class the center node 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)