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 "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.

Includes members:
  Member "detecting and characterizing lateral phishing at scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lateral astroturfing attacks on twitter trending topics")
  Member "gradient regularized budgeted boosting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust decision trees against adversarial examples")
  Member "robust decision trees against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "robustness verification of tree based models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial robustness toolbox v1 0 0")
  Member "multi layered gradient boosting decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpretation and simplification of deep forest")
  Member "adgraph a graph based approach to ad and tracker blocking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a4 evading learning based adblockers")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")

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.CR(Cryptography and Security)