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: "training robust tree ensembles for security"
  Abstract: Tree ensemble models including random forests and gradient boosted decision trees, are widely used as security classifiers to detect malware, phishing, scam, social engineering, etc. However, the robustness of tree ensembles has not been thoroughly studied. Existing approaches mainly focus on adding more robust features and conducting feature ablation study, which do not provide robustness guarantee against strong adversaries. #R##N#In this paper, we propose a new algorithm to train robust tree ensembles. Robust training maximizes the defender's gain as if the adversary is trying to minimize that. We design a general algorithm based on greedy heuristic to find better solutions to the minimization problem than previous work. We implement the algorithm for gradient boosted decision trees in xgboost and random forests in scikit-learn. Our evaluation over benchmark datasets show that, we can train more robust models than the start-of-the-art robust training algorithm in gradient boosted decision trees, with a 1.26X increase in the $L_\infty$ evasion distance required for the strongest whitebox attacker. In addition, our algorithm is general across different gain metrics and types of tree ensembles. We achieve 3.32X increase in $L_\infty$ robustness distance compared to the baseline random forest training method. #R##N#Furthermore, to make the robustness increase meaningful in security applications, we propose attack-cost-driven constraints for the robust training process. Our training algorithm maximizes attacker's evasion cost by integrating domain knowledge about feature manipulation costs. We use twitter spam detection as a case study to analyze attacker's cost increase to evade our robust model. Our technique can train robust model to rank robust features as most important ones, and our robust model requires about 8.4X increase in attacker's economic cost to be evaded compared to the baseline.

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)