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: "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
  Abstract: Iterative algorithms, like gradient descent, are common tools for solving a variety of problems, such as model fitting. For this reason, there is interest in creating differentially private versions of them. However, their conversion to differentially private algorithms is often naive. For instance, a fixed number of iterations are chosen, the privacy budget is split evenly among them, and at each iteration, parameters are updated with a noisy gradient. In this paper, we show that gradient-based algorithms can be improved by a more careful allocation of privacy budget per iteration. Intuitively, at the beginning of the optimization, gradients are expected to be large, so that they do not need to be measured as accurately. However, as the parameters approach their optimal values, the gradients decrease and hence need to be measured more accurately. We add a basic line-search capability that helps the algorithm decide when more accurate gradient measurements are necessary. Our gradient descent algorithm works with the recently introduced zCDP version of differential privacy. It outperforms prior algorithms for model fitting and is competitive with the state-of-the-art for $(\epsilon,\delta)$-differential privacy, a strictly weaker definition than zCDP.

Connected through hyperedge HE-1 (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-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"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "differentially private fair learning"

Connected through hyperedge HE-3 (induced by source paper "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"):
  with members:
    - "efficient privacy preserving nonconvex optimization"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "laplacian smoothing gradient descent"
    - "differentially private federated learning a client level perspective"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "local differential privacy for physical sensor data and sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "gradient perturbation is underrated for differentially private convex optimization"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "privacy amplification by iteration"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "efficient privacy preserving nonconvex optimization"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "differentially private generative adversarial network"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-6 (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"
    - "sok security and privacy in machine learning"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "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-7 (induced by source paper "renyi differentially private admm based l1 regularized classification"):
  with members:
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (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"
    - "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"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"

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.LG(Machine Learning)