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: "privacy preserving collaborative deep learning with irregular participants"
  Abstract: With large amounts of data collected from massive sensors, mobile users and institutions becomes widely available, neural network based deep learning is becoming increasingly popular and making great success in many application scenarios, such as image detection, speech recognition and machine translation. While deep learning can provide various benefits, the data for training usually contains highly sensitive information, e.g., personal medical records, and a central location for saving the data may pose a considerable threat to user privacy. #R##N#In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users (i.e., participants) to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $\epsilon$-differential privacy. In particular, for the first time, we consider the possibility that the data of certain participants may be of low quality (called \textit{irregular participants}), and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world data sets for regression and classification tasks. The results demonstrate that our system is robust to irregular participants, and can achieve high accuracy close to the centralized model while ensuring rigorous privacy protection.

Connected through hyperedge HE-1 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"
    - "detecting violations of differential privacy"

Connected through hyperedge HE-2 (induced by source paper "preserving differential privacy in adversarial learning with provable robustness"):
  with members:
    - "sok security and privacy in machine learning"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "extending defensive distillation"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "robustness to adversarial examples through an ensemble of specialists"

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)