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 algorithms matching problems and beyond"):
  Abstract: In discrete search and optimization problems where the elements that may or not be included in a solution correspond to humans, individual fairness needs to be expressed in terms of each individual's satisfaction probability (the probability of being included in the solution). In this paper we introduce the maxmin fairness framework which provides, on any given input instance, the strongest guarantee possible for all individuals, in terms of satisfaction probability. #R##N#A probability distribution over valid 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. We provide an efficient exact algorithm for maxmin-fair bipartite matching combining flow-based methods with algorithms for edge-coloring regular bipartite graphs. As shown in our experimental evaluation, our algorithm scales to graphs with millions of vertices and hundreds of millions of edges, taking only a few minutes on a simple architecture. #R##N#We generalize our method to the case where the structure of valid solutions forms a matroid, in which case the price of fairness is zero, and show that then maxmin-fair distributions mini- mize social inequality among Pareto-efficient distributions. More generally, we prove that a maxmin-fair distribution of solutions to any combinatorial search problem may be found efficiently under the sole assumption that, given an arbitrary assignment of non-negative weights to individuals, a maximum-weight solution may be found in polynomial time. This class of problems extends beyond matchings and matroids, and includes the vast majority of search problems for which exact algorithms are known.

Includes members:
  Member "nash social welfare approximation for strategic agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair by design matching")
  Member "fairness through awareness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic fairness")

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)