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: "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
  Abstract: Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) has much lower utility than the non-private ones. To mitigate this degradation, we propose a DP Laplacian smoothing SGD (DP-LSSGD) for privacy-preserving ML. At the core of DP-LSSGD is the Laplace smoothing operator, which smooths out the Gaussian noise vector used in the Gaussian mechanism. Under the same amount of noise used in the Gaussian mechanism, DP-LSSGD attains the same differential privacy guarantee, but a strictly better utility guarantee, excluding an intrinsic term which is usually dominated by the other terms, for convex optimization than DP-SGD by a factor which is much less than one. In practice, DP-LSSGD makes training both convex and nonconvex ML models more efficient and enables the trained models to generalize better. For ResNet20, under the same strong differential privacy guarantee, DP-LSSGD can lift the testing accuracy of the trained private model by more than $8$\% compared with DP-SGD. The proposed algorithm is simple to implement and the extra computational complexity and memory overhead compared with DP-SGD are negligible. DP-LSSGD is applicable to train a large variety of ML models, including deep neural nets. The code is available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"):
  with members:
    - "robust and communication efficient collaborative learning"
    - "an exact quantized decentralized gradient descent algorithm"
    - "communication compression for decentralized training"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "leasgd an efficient and privacy preserving decentralized algorithm for distributed learning"
    - "gradient sparsification for communication efficient distributed optimization"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"

Connected through hyperedge HE-2 (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"
    - "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"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "laplacian smoothing stochastic gradient markov chain monte carlo"):
  with members:
    - "a deterministic approach to avoid saddle points"
    - "laplacian smoothing gradient descent"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

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)