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: "multiwinner voting with fairness constraints"
  Abstract: Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters. However, if candidates have sensitive attributes such as gender or ethnicity (when selecting a committee), or specified types such as political leaning (when selecting a subset of news items), an algorithm that chooses a subset by optimizing a multiwinner voting rule may be unbalanced in its selection -- it may under or over represent a particular gender or political orientation in the examples above. We introduce an algorithmic framework for multiwinner voting problems when there is an additional requirement that the selected subset should be "fair" with respect to a given set of attributes. Our framework provides the flexibility to (1) specify fairness with respect to multiple, non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score function. We study the computational complexity of this constrained multiwinner voting problem for monotone and submodular score functions and present several approximation algorithms and matching hardness of approximation results for various attribute group structure and types of score functions. We also present simulations that suggest that adding fairness constraints may not affect the scores significantly when compared to the unconstrained case.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fair clustering with multiple colors"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "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"

Connected through hyperedge HE-4 (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"
    - "coresets for clustering with fairness constraints"
    - "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"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-5 (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-6 (induced by source paper "stable and fair classification"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "fair and diverse dpp based data summarization"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "generalization bounds for uniformly stable algorithms"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "certified defenses for data poisoning attacks"

Connected through hyperedge HE-7 (induced by source paper "fairness aware ranking in search recommendation systems with application to linkedin talent search"):
  with members:
    - "talent search and recommendation systems at linkedin practical challenges and lessons learned"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "ranking with fairness constraints"
    - "learning non discriminatory predictors"
    - "algorithmic decision making and the cost of fairness"
    - "fa ir a fair top k ranking algorithm"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-8 (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"

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.CY(Computers and Society)