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: "differentially private empirical risk minimization efficient algorithms and tight error bounds"
  Abstract: In this paper, we initiate a systematic investigation of differentially private algorithms for convex empirical risk minimization. Various instantiations of this problem have been studied before. We provide new algorithms and matching lower bounds for private ERM assuming only that each data point's contribution to the loss function is Lipschitz bounded and that the domain of optimization is bounded. We provide a separate set of algorithms and matching lower bounds for the setting in which the loss functions are known to also be strongly convex. #R##N#Our algorithms run in polynomial time, and in some cases even match the optimal non-private running time (as measured by oracle complexity). We give separate algorithms (and lower bounds) for $(\epsilon,0)$- and $(\epsilon,\delta)$-differential privacy; perhaps surprisingly, the techniques used for designing optimal algorithms in the two cases are completely different. #R##N#Our lower bounds apply even to very simple, smooth function families, such as linear and quadratic functions. This implies that algorithms from previous work can be used to obtain optimal error rates, under the additional assumption that the contributions of each data point to the loss function is smooth. We show that simple approaches to smoothing arbitrary loss functions (in order to apply previous techniques) do not yield optimal error rates. In particular, optimal algorithms were not previously known for problems such as training support vector machines and the high-dimensional median.

Connected through hyperedge HE-1 (induced by source paper "differentially private deep learning with smooth sensitivity"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning differentially private recurrent language models"
    - "learning privately from multiparty data"
    - "deep residual learning for image recognition"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "differentially private model publishing for deep learning"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "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"
    - "tagvisor a privacy advisor for sharing hashtags"

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 "private stochastic convex optimization with optimal rates"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-5 (induced by source paper "gradient perturbation is underrated for differentially private convex optimization"):
  with members:
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "private deep learning with teacher ensembles"):
  with members:
    - "private model compression via knowledge distillation"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial dropout for supervised and semi supervised learning"
    - "learning differentially private recurrent language models"
    - "learning privately from multiparty data"
    - "temporal ensembling for semi supervised learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "parameter free locally differentially private stochastic subgradient descent"):
  with members:
    - "parameter free online convex optimization with sub exponential noise"
    - "black box reductions for parameter free online learning in banach spaces"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "learning from data with heterogeneous noise using sgd"
    - "no regret algorithms for unconstrained online convex optimization"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving off policy evaluation"):
  with members:
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "stochastic variance reduction methods for policy evaluation"
    - "deep reinforcement learning for sepsis treatment"
    - "stochastic variance reduction methods for saddle point problems"
    - "concentrated differential privacy"
    - "calibrating data to sensitivity in private data analysis"

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)