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 "stackelberg shortest path tree game revisited"):
  Abstract: Let G(V;E) be a directed graph with n vertices and m edges. The edges E of G are divided into two types: EF and EP . Each edge of EF has a xed price. The edges of EP are the priceable edges and their price is not xed a priori. Let r be a vertex of G. For an assignment of prices to the edges of EP , the revenue is given by the following procedure: select a shortest path tree T from r with respect to the prices (a tree of cheapest paths); the revenue is the sum, over all priceable edges e, of the product of the price of e and the number of vertices below e in T . Assuming that k = jEPj 2 is a constant, we provide a data structure whose construction takesO(m+n log k1 n) time and with the property that, when we assign prices to the edges of EP , the revenue can be computed in (log k1 n). Using our data structure, we save almost a linear factor when computing the optimal strategy in the Stackelberg shortest paths tree game of [D. Bil

Includes members:
  Member "the stackelberg minimum spanning tree game":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "negative prices in network pricing games")
  Member "stackelberg network pricing is hard to approximate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "negative prices in network pricing games")
  Member "improved hardness of approximation for stackelberg shortest path pricing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "negative prices in network pricing games")
  Member "stackelberg network pricing games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "negative prices in network pricing games")

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)