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: "membership inference attacks against machine learning models"
  Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. #R##N#We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

Connected through hyperedge HE-1 (induced by source paper "the future of digital health with federated learning"):
  with members:
    - "multi site fmri analysis using privacy preserving federated learning and domain adaptation abide results"
    - "overcoming forgetting in federated learning on non iid data"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "privacy preserving federated brain tumour segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "peer to peer federated learning on graphs"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"

Connected through hyperedge HE-2 (induced by source paper "dynamic backdoor attacks against machine learning models"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "strip a defence against trojan attacks on deep neural networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "the natural auditor how to tell if someone used your words to train their model"

Connected through hyperedge HE-3 (induced by source paper "wireless federated learning with local differential privacy"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "differential privacy enabled federated learning for sensitive health data"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "federated learning with bayesian differential privacy"

Connected through hyperedge HE-4 (induced by source paper "survey of personalization techniques for federated learning"):
  with members:
    - "three approaches for personalization with applications to federated learning"
    - "personalized federated learning for intelligent iot applications a cloud edge based framework"
    - "data selection for federated learning with relevant and irrelevant data at clients"
    - "federated learning of a mixture of global and local models"
    - "salvaging federated learning by local adaptation"
    - "personalized federated learning a meta learning approach"
    - "federated learning with personalization layers"
    - "adaptive gradient based meta learning methods"

Connected through hyperedge HE-5 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation"):
  with members:
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "that which we call private"
    - "private stochastic convex optimization with optimal rates"
    - "evaluating differentially private machine learning in practice"
    - "differentially private summation with multi message shuffling"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"

Connected through hyperedge HE-6 (induced by source paper "performance analysis and optimization in privacy preserving federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "on safeguarding privacy and security in the framework of federated learning"
    - "federated reinforcement learning"
    - "deep leakage from gradients"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "the value of collaboration in convex machine learning with differential privacy"
    - "federated learning challenges methods and future directions"
    - "age based scheduling policy for federated learning in mobile edge networks"

Connected through hyperedge HE-7 (induced by source paper "threats to federated learning a survey"):
  with members:
    - "fedvision an online visual object detection platform powered by federated learning"
    - "idlg improved deep leakage from gradients"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "towards fair and decentralized privacy preserving deep learning with blockchain"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "analyzing federated learning through an adversarial lens"

Connected through hyperedge HE-8 (induced by source paper "classifying the classifier dissecting the weight space of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology of learning in artificial neural networks"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "taskonomy disentangling task transfer learning"
    - "darts differentiable architecture search"
    - "do convolutional neural networks learn class hierarchy"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting deep visual representations via network dissection"

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)