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 coresets and streaming algorithms for fair k means clustering"
  Abstract: We study fair clustering problems as proposed by Chierichetti et al. (NIPS 2017). Here, points have a sensitive attribute and all clusters in the solution are required to be balanced with respect to it (to counteract any form of data-inherent bias). Previous algorithms for fair clustering do not scale well. #R##N#We show how to model and compute so-called coresets for fair clustering problems, which can be used to significantly reduce the input data size. We prove that the coresets are composable and show how to compute them in a streaming setting. Furthermore, we propose a variant of Lloyd's algorithm that computes fair clusterings and extend it to a fair k-means++ clustering algorithm. We implement these algorithms and provide empirical evidence that the combination of our approximation algorithms and the coreset construction yields a scalable algorithm for fair k-means clustering.

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"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "counterpoint by convolution"
    - "fair clustering through fairlets"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "datasheets for datasets"
    - "what is the effect of importance weighting in deep learning"
    - "learning beyond human expertise with generative models for dental restorations"

Connected through hyperedge HE-3 (induced by source paper "r enyi fair inference"):
  with members:
    - "fair logistic regression an adversarial perspective"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "scalable fair clustering"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "tunable measures for information leakage and applications to privacy utility tradeoffs"
    - "fair clustering through fairlets"

Connected through hyperedge HE-4 (induced by source paper "fair algorithms for clustering"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "constant factor fpt approximation for capacitated k median"
    - "a unified framework for clustering constrained data without locality property"
    - "fair clustering through fairlets"
    - "generalized center problems with outliers"

Connected through hyperedge HE-5 (induced by source paper "coresets for clustering with fairness constraints"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "principal fairness removing bias via projections"
    - "scalable fair clustering"
    - "clustering without over representation"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair and diverse dpp based data summarization"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"

Connected through hyperedge HE-6 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "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"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-7 (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"
    - "fair algorithms for clustering"
    - "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"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-8 (induced by source paper "fairness in clustering with multiple sensitive attributes"):
  with members:
    - "proportionally fair clustering"
    - "principal fairness removing bias via projections"
    - "fair algorithms for clustering"
    - "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"

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)