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 "an iterative security game for computing robust and adaptive network flows"):
  Abstract: The recent advancement in cyberphysical systems has led to an exponential growth in the use of automated devices which in turn has created new security challenges. By manipulating cyberphysical components, a potential attacker can modify the capacities of multiple edges so as to disrupt the network of interest. Existing robust network flow models typically assume that the entire flow of an attacked edge gets lost. However, in many practical systems, the flow of an attacked edge could potentially be rerouted through adjacent edges with residual capacity. In order to address this feature, we propose a robust and adaptive network flow model to effectively counter possible attacking behaviors of an adversary operating under a budget constraint. Specifically, we introduce a novel scenario generation approach based on an iterative two-player game between a defender and an adversary. We assume that the adversary always takes a best response (out of some feasible attacking scenarios) against the current flow scenario prepared by the defender. On the other hand, we assume that the defender considers all the attacking behaviors revealed by the adversary in previous iterations in order to generate a new robust (maximin) flow strategy. This iterative game continues until the objectives of both the players converge. We show that the robust and adaptive network flow problem is NP-hard and that the complexity of the adversary's decision problem grows exponentially with the network size and the adversary's budget value. We propose two principled heuristic approaches for solving the adversary's problem at the scale of a large urban network. Extensive computational results on multiple synthetic and real-world data sets demonstrate that the solution provided by the defender's problem significantly increases the amount of flow pushed through the network over four state-of-the-art benchmark approaches.

Includes members:
  Member "probability distributions on partially ordered sets and network security games":
    also connected through other hyperedges:
      (none)
  Member "signaling game based misbehavior inspection in v2i enabled highway operations":
    also connected through other hyperedges:
      (none)
  Member "securing infrastructure facilities when does proactive defense help":
    also connected through other hyperedges:
      (none)
  Member "network flow routing under strategic link disruptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "securing infrastructure facilities when does proactive defense help")
  Member "on the power of randomization in network interdiction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the value of randomized strategies in distributionally robust risk averse network interdiction 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.GT(Computer Science and Game Theory)