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 "privacy preserving gradient boosting decision trees"):
  Abstract: The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be further reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines.

Includes members:
  Member "a survey on federated learning systems vision hype and reality for data privacy and protection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "practical federated gradient boosting decision trees":
    also connected through other hyperedges:
      (none)
  Member "multi layered gradient boosting decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretation and simplification of deep forest")
  Member "towards demystifying membership inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data and model dependencies of membership inference attack")
  Member "catboost unbiased boosting with categorical features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smart data based ensemble for imbalanced big data classification")
  Member "differentially private mixture of generative neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "techniques d anonymisation tabulaire concepts et mise en oeuvre")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "membership inference attacks against machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the future of digital health with federated learning")

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.LG(Machine Learning)