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: "mathsf qflip an adaptive reinforcement learning strategy for the mathsf flipit security game"
  Abstract: A rise in Advanced Persistent Threats (APTs) has introduced a need for robustness against long-running, stealthy attacks which circumvent existing cryptographic security guarantees. \(\mathsf {FlipIt}\) is a security game that models attacker-defender interactions in advanced scenarios such as APTs. Previous work analyzed extensively non-adaptive strategies in \(\mathsf {FlipIt}\), but adaptive strategies rise naturally in practical interactions as players receive feedback during the game. We model the \(\mathsf {FlipIt}\) game as a Markov Decision Process and introduce \(\mathsf {QFlip}\), an adaptive strategy for \(\mathsf {FlipIt}\) based on temporal difference reinforcement learning. We prove theoretical results on the convergence of our new strategy against an opponent playing with a Periodic strategy. We confirm our analysis experimentally by extensive evaluation of \(\mathsf {QFlip}\) against specific opponents. \(\mathsf {QFlip}\) converges to the optimal adaptive strategy for Periodic and Exponential opponents using associated state spaces. Finally, we introduce a generalized \(\mathsf {QFlip}\) strategy with composite state space that outperforms a Greedy strategy for several distributions including Periodic and Uniform, without prior knowledge of the opponent’s strategy. We also release an OpenAI Gym environment for \(\mathsf {FlipIt}\) to facilitate future research.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning based adaptive moving target defense"):
  with members:
    - "finding needles in a moving haystack prioritizing alerts with adversarial reinforcement learning"
    - "markov decision process to enforce moving target defence policies"
    - "optimal timing of moving target defense a stackelberg game model"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

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)