human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the power of randomization in network interdiction"
  Abstract: Network interdiction can be viewed as a game between two players, an "interdictor" and a "flow player". The flow player wishes to send as much material as possible through a network, while the interdictor attempts to minimize the amount of transported material by removing a certain number of arcs, say $\Gamma$ arcs. We introduce the randomized network interdiction problem that allows the interdictor to use randomness to select arcs to be removed. We model the problem in two different ways: arc-based and path-based formulations, depending on whether flows are defined on arcs or paths, respectively. We present insights into the modeling power, complexity, and approximability of both formulations. In particular, we prove that $Z_{\text{NI}}/Z_{\text{RNI}}\leq \Gamma+1$, $Z_{\text{NI}}/Z_{\text{RNI}}^{\text{Path}}\leq \Gamma+1$, $Z_{\text{RNI}}/Z_{\text{RNI}}^{\text{Path}}\leq \Gamma$, where $Z_{\text{NI}}$, $Z_{\text{RNI}}$, and $Z_{\text{RNI}}^{\text{Path}}$ are the optimal values of the network interdiction problem and its randomized versions in arc-based and path-based formulations, respectively. We also show that these bounds are tight. We show that it is NP-hard to compute the values $Z_{\text{RNI}}$ and $Z_{\text{RNI}}^{\text{Path}}$ for a general $\Gamma$, but they are computable in polynomial time when $\Gamma=1$. Further, we provide a $(\Gamma+1)$-approximation for $Z_{\text{NI}}$, a $\Gamma$-approximation for $Z_{\text{RNI}}$, and a $\big(1+\lfloor \Gamma/2\rfloor \cdot \lceil \Gamma/2\rceil/(\Gamma+1)\big)$-approximation for $Z_{\text{RNI}}^{\text{Path}}$.

Connected through hyperedge HE-1 (induced by source paper "the value of randomized strategies in distributionally robust risk averse network interdiction games"):
  with members:
    - "distributionally robust game theory"

Connected through hyperedge HE-2 (induced by source paper "an iterative security game for computing robust and adaptive network flows"):
  with members:
    - "probability distributions on partially ordered sets and network security games"
    - "signaling game based misbehavior inspection in v2i enabled highway operations"
    - "securing infrastructure facilities when does proactive defense help"
    - "network flow routing under strategic link disruptions"

Connected through hyperedge HE-3 (induced by source paper "network interdiction using adversarial traffic flows"):
  with members:
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

Connected through hyperedge HE-4 (induced by source paper "a network monitoring game with heterogeneous component criticality levels"):
  with members:
    - "network inspection for detecting strategic attacks"

Connected through hyperedge HE-5 (induced by source paper "the complexity of computing a robust flow"):
  with members:
    - "robust flows over time models and complexity results"
    - "rerouting flows when links fail"
    - "protection of flows under targeted attacks"
    - "hardness and approximation for network flow interdiction"

Connected through hyperedge HE-6 (induced by source paper "robust randomized matchings"):
  with members:
    - "randomized minmax regret for combinatorial optimization under uncertainty"
    - "packing a knapsack of unknown capacity"
    - "a simple ptas for weighted matroid matching on strongly base orderable matroids"

Connected through hyperedge HE-7 (induced by source paper "network monitoring under strategic disruptions"):
  with members:
    - "robust monotone submodular function maximization"
    - "network inspection for detecting strategic attacks"

Connected through hyperedge HE-8 (induced by source paper "computational methods for path based robust flows"):
  with members:
    - "robust flows over time models and complexity results"

Question: Please tell me which class the center node 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)