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 "fair by design matching"):
  Abstract: Matching algorithms are used routinely to match donors to recipients for solid organs transplantation, for the assignment of medical residents to hospitals, record linkage in databases, scheduling jobs on machines, network switching, online advertising, and image recognition, among others. Although many optimal solutions may exist to a given matching problem, when the elements that shall or not be included in a solution correspond to individuals, it becomes of paramount importance that the solution be selected fairly. In this paper we study individual fairness in matching problems. Given that many maximum matchings may exist, each one satisfying a different set of individuals, the only way to guarantee fairness is through randomization. Hence we introduce the distributional maxmin fairness framework which provides, for any given input instance, the strongest guarantee possible simultaneously for all individuals in terms of satisfaction probability (the probability of being matched in the solution). Specifically, a probability distribution over feasible solutions is maxmin-fair if it is not possible to improve the satisfaction probability of any individual without decreasing it for some other individual which is no better off. In the special case of matchings in bipartite graphs, our framework is equivalent to the egalitarian mechanism of Bogomolnaia and Mouline. Our main contribution is a polynomial-time algorithm for fair matching building on techniques from minimum cuts, and edge-coloring algorithms for regular bipartite graphs, and transversal theory. For bipartite graphs, our algorithm runs in $O((|V|^2 + |E||V|^{2/3}) \cdot (\log |V|)^2)$ expected time and scales to graphs with tens of millions of vertices and hundreds of millions of edges. To the best of our knowledge, this provides the first large-scale implementation of the egalitarian mechanism.

Includes members:
  Member "fairness behind a veil of ignorance a welfare analysis for automated decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair generative modeling via weak supervision")
  Member "balancing lexicographic fairness and a utilitarian objective with application to kidney exchange":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable robust kidney exchange")
  Member "algorithmic decision making and the cost of fairness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "fairness in learning classic and contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "nash social welfare approximation for strategic agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotonicity and competitive equilibrium in cake cutting")
  Member "fairness through awareness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "perfect matchings in o n 1 5 time in regular bipartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")

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)