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: "boosting privately privacy preserving federated extreme boosting for mobile crowdsensing"
  Abstract: The state-of-the-art federated learning brings a new direction for the data privacy protection of mobile crowdsensing machine learning applications. However, besides being vulnerable to GAN based user data construction attack, the existing gradient descent based federate learning schemes are lack of consideration for how to preserve the model privacy. In this paper, we propose a secret sharing based federated extreme boosting learning frame-work (FedXGB) to achieve privacy-preserving model training for mobile crowdsensing. First, a series of protocols are designed to implement privacy-preserving extreme gradient boosting of classification and regression tree. The protocols preserve the user data privacy protection feature of federated learning that XGBoost is trained without revealing plaintext user data. Then, in consideration of the high commercial value of a well-trained model, a secure prediction protocol is developed to protect the model privacy for the crowdsensing sponsor. Additionally, we operate comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness and efficiency of FedXGB. The results show that FedXGB is secure in the honest-but-curious model, and attains approximate accuracy and convergence rate with the original model in low runtime.

Connected through hyperedge HE-1 (induced by source paper "revocable federated learning a benchmark of federated forest"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "towards federated learning at scale system design"
    - "evaluating differentially private machine learning in practice"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "machine learning in compiler optimisation"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "switching convolutional neural network for crowd counting"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-2 (induced by source paper "practical federated gradient boosting decision trees"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "secure federated transfer learning"
    - "federated multi task learning"
    - "xgboost a scalable tree boosting system"
    - "federated learning of deep networks using model averaging"

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)