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: "learning in a large function space privacy preserving mechanisms for svm learning"
  Abstract: Several recent studies in privacy-preserving learning have considered the trade-off between utility or risk and the level of differential privacy guaranteed by mechanisms for statistical query processing. In this paper we study this trade-off in private Support Vector Machine (SVM) learning. We present two efficient mechanisms, one for the case of finite-dimensional feature mappings and one for potentially infinite-dimensional feature mappings with translation-invariant kernels. For the case of translation-invariant kernels, the proposed mechanism minimizes regularized empirical risk in a random Reproducing Kernel Hilbert Space whose kernel uniformly approximates the desired kernel with high probability. This technique, borrowed from large-scale learning, allows the mechanism to respond with a finite encoding of the classifier, even when the function class is of infinite VC dimension. Differential privacy is established using a proof technique from algorithmic stability. Utility--the mechanism's response function is pointwise epsilon-close to non-private SVM with probability 1-delta--is proven by appealing to the smoothness of regularized empirical risk minimization with respect to small perturbations to the feature mapping. We conclude with a lower bound on the optimal differential privacy of the SVM. This negative result states that for any delta, no mechanism can be simultaneously (epsilon,delta)-useful and beta-differentially private for small epsilon and small beta.

Connected through hyperedge HE-1 (induced by source paper "differentially private model publishing for deep learning"):
  with members:
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "stealing machine learning models via prediction apis"
    - "concentrated differential privacy"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "chasing accuracy and privacy and catching both a literature survey on differentially private histogram publication"):
  with members:
    - "privacy preserving mhealth data release with pattern consistency"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "differentially private k means clustering"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-3 (induced by source paper "adaclip adaptive clipping for private sgd"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "differentially private releasing via deep generative model technical report"
    - "federated learning for mobile keyboard prediction"
    - "optimal noise adding mechanism in additive differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-4 (induced by source paper "real world image datasets for federated learning"):
  with members:
    - "towards federated learning at scale system design"
    - "secureboost a lossless federated learning framework"
    - "federated learning of out of vocabulary words"
    - "a generic framework for privacy preserving deep learning"
    - "yolov3 an incremental improvement"
    - "leaf a benchmark for federated settings"
    - "federated learning for mobile keyboard prediction"
    - "object detection with deep learning a review"

Connected through hyperedge HE-5 (induced by source paper "differentially private distributed data summarization under covariate shift"):
  with members:
    - "clustering algorithms for the centralized and local models"
    - "learning privately from multiparty data"
    - "differentially private stochastic gradient descent for in rdbms analytics"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep domain confusion maximizing for domain invariance"
    - "the composition theorem for differential privacy"
    - "a simple and practical algorithm for differentially private data release"

Connected through hyperedge HE-6 (induced by source paper "stolen memories leveraging model memorization for calibrated white box membership inference"):
  with members:
    - "a general approach to adding differential privacy to iterative training procedures"
    - "understanding membership inferences on well generalized learning models"
    - "on calibration of modern neural networks"
    - "towards measuring membership privacy"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "stealing machine learning models via prediction apis"
    - "revisiting differentially private regression lessons from learning theory and their consequences"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving public release of datasets for support vector machine classification"):
  with members:
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
    - "gaussian error linear units gelus"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "functional mechanism regression analysis under differential privacy"

Connected through hyperedge HE-8 (induced by source paper "locally differentially private naive bayes classification"):
  with members:
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "differentially private empirical risk minimization"

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)