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: "adaptive laplace mechanism differential privacy preservation in deep learning"
  Abstract: In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "removing disparate impact of differentially private stochastic gradient descent on model accuracy"):
  with members:
    - "differentially private and fair classification via calibrated functional mechanism"
    - "differential privacy has disparate impact on model accuracy"
    - "differentially private learning with adaptive clipping"
    - "robust anomaly detection and backdoor attack detection via differential privacy"
    - "adaclip adaptive clipping for private sgd"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-3 (induced by source paper "differentially private erm based on data perturbation"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "differentially private bayesian linear regression"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "efficient private erm for smooth objectives"
    - "collect at once use effectively making non interactive locally private learning possible"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-4 (induced by source paper "preserving differential privacy in adversarial learning with provable robustness"):
  with members:
    - "privacy preserving collaborative deep learning with irregular participants"
    - "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"
    - "robustness to adversarial examples through an ensemble of specialists"

Connected through hyperedge HE-5 (induced by source paper "heterogeneous gaussian mechanism preserving differential privacy in deep learning with provable robustness"):
  with members:
    - "differentially private model publishing for deep learning"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "sok security and privacy in machine learning"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-6 (induced by source paper "an adaptive and fast convergent approach to differentially private deep learning"):
  with members:
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "privacy preserving machine learning through data obfuscation"
    - "boosting model performance through differentially private model aggregation"
    - "renyi differential privacy"
    - "geographic differential privacy for mobile crowd coverage maximization"

Connected through hyperedge HE-7 (induced by source paper "dpugc learn differentially private representation for user generated contents"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "shaped shared private encoder decoder for text style adaptation"
    - "detecting communities under differential privacy"
    - "enriching word vectors with subword information"
    - "nonparametric spherical topic modeling with word embeddings"
    - "document classification by inversion of distributed language representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "lep cnn a lightweight edge device assisted privacy preserving cnn inference solution for iot"):
  with members:
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "understanding deep image representations by inverting them"

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)