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 "minimum entropy submodular optimization and fairness in cooperative games"):
  Abstract: We study minimum entropy submodular optimization, a common generalization of the minimum entropy set cover problem, studied earlier by Cardinal et al., and the submodular set cover problem (Wolsey [Wol82], Fujishige [BIKP01], etc). We give a general bound of the approximation performance of the greedy algorithm using an approach that can be interpreted in terms of a particular type of biased network ows. As an application we rederive known results for the Minimum Entropy Set Cover and Minimum Entropy Orientation problems, and obtain a nontrivial bound for a new problem called the Minimum Entropy Spanning Tree problem. The problem can be applied to (and is partly motivated by) the denition of worst-case approaches to fairness in concave cooperative games, similar to the notion of price of anarchy in noncooperative settings.

Includes members:
  Member "a parametric worst case approach to fairness in tu cooperative games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved approximation algorithms for low density instances of the minimum entropy set cover problem")
  Member "minimum entropy combinatorial optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the entropy of couplings")
  Member "partition reduction for lossy data compression problem":
    also connected through other hyperedges:
      (none)
  Member "interactive submodular set cover":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "adaptive submodularity theory and applications in active learning and stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling")
  Member "minimum entropy orientations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved approximation algorithms for low density instances of the minimum entropy set cover problem")
  Member "information inequalities for joint distributions with interpretations and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual loomis whitney inequalities via information theory")

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)