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: "adaptive attacker strategy development against moving target cyber defenses"
  Abstract: A model of strategy formulation is used to study how an adaptive attacker learns to overcome a moving target cyber defense. The attacker-defender interaction is modeled as a game in which a defender deploys a temporal platform migration defense. Against this defense, a population of attackers develop strategies specifying the temporal ordering of resource investments that bring targeted zero-day exploits into existence. Attacker response to two defender temporal platform migration scheduling policies are examined. In the first defender scheduling policy, the defender selects the active platform in each match uniformly at random from a pool of available platforms. In the second policy the defender schedules each successive platform to maximize the diversity of the source code presented to the attacker. Adaptive attacker response strategies are modeled by finite state machine (FSM) constructs that evolve during simulated play against defender strategies via an evolutionary algorithm. It is demonstrated that the attacker learns to invest heavily in exploit creation for the platform with the least similarity to other platforms when faced with a diversity defense, while avoiding investment in exploits for this least similar platform when facing a randomization defense. Additionally, it is demonstrated that the diversity-maximizing defense is superior for shorter duration attacker-defender engagements, but performs sub-optimally in extended attacker-defender interactions.

Connected through hyperedge HE-1 (induced by source paper "moving target defense for web applications using bayesian stackelberg games"):
  with members:
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

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.CR(Cryptography and Security)