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: "fair algorithms for clustering"
  Abstract: We study clustering problems under the lens of {\em algorithmic fairness} inspired by the disparate impact doctrine. Given a collection of points containing many {\em protected groups}, the goal is to find good clustering solutions where each cluster {\em fairly represents} each group. We allow the user to specify the parameters that define fair representation, and this flexibility makes our model significantly more general than the recent models of Chierichetti et al. (NIPS 2017) and Rosner and Schmidt (ICALP 2018). Our main result is a simple algorithm that, for any $\ell_p$-norm including the $k$-center, $k$-median, and $k$-means objectives, transforms any clustering solution to a fair one with only a slight loss in quality.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "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"
    - "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 "explainable k means and k medians clustering"):
  with members:
    - "individual fairness for k clustering"
    - "coresets for clustering with fairness constraints"
    - "same cluster querying for overlapping clusters"
    - "online k means 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-3 (induced by source paper "proportionally fair clustering"):
  with members:
    - "privacy preserving clustering with constraints"
    - "markets for public decision making"
    - "fair clustering through fairlets"
    - "fair allocation of indivisible public goods"
    - "on fairness and calibration"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "algorithmic decision making and the cost of fairness"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-4 (induced by source paper "fairness in representation quantifying stereotyping as a representational harm"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "understanding the origins of bias in word embeddings"
    - "convex formulations for fair principal component analysis"
    - "fair clustering through fairlets"
    - "the price of fair pca one extra dimension"
    - "learning adversarially fair and transferable representations"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-5 (induced by source paper "near neighbor who is the fairest of them all"):
  with members:
    - "scalable fair clustering"
    - "independent range sampling revisited again"
    - "fair near neighbor search independent range sampling in high dimensions"
    - "convex formulations for fair principal component analysis"
    - "fair clustering through fairlets"
    - "fair algorithms for learning in allocation problems"
    - "a reductions approach to fair classification"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-6 (induced by source paper "clustering without over representation"):
  with members:
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair clustering through fairlets"
    - "language modeling by clustering with word embeddings for text readability assessment"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-7 (induced by source paper "fairness in clustering with multiple sensitive attributes"):
  with members:
    - "proportionally fair clustering"
    - "principal fairness removing bias via projections"
    - "towards fair deep clustering with multi state protected variables"
    - "scalable fair clustering"
    - "clustering with fairness constraints a flexible and scalable approach"
    - "clustering without over representation"
    - "privacy preserving clustering with constraints"
    - "convex formulations for fair principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "scalable fair clustering"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair clustering through fairlets"
    - "the frontiers of fairness in machine learning"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"

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)