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: "revisiting differentially private regression lessons from learning theory and their consequences"
  Abstract: Private regression has received attention from both database and security communities. Recent work by Fredrikson et al. (USENIX Security 2014) analyzed the functional mechanism (Zhang et al. VLDB 2012) for training linear regression models over medical data. Unfortunately, they found that model accuracy is already unacceptable with differential privacy when $\varepsilon = 5$. We address this issue, presenting an explicit connection between differential privacy and stable learning theory through which a substantially better privacy/utility tradeoff can be obtained. Perhaps more importantly, our theory reveals that the most basic mechanism in differential privacy, output perturbation, can be used to obtain a better tradeoff for all convex-Lipschitz-bounded learning tasks. Since output perturbation is simple to implement, it means that our approach is potentially widely applicable in practice. We go on to apply it on the same medical data as used by Fredrikson et al. Encouragingly, we achieve accurate models even for $\varepsilon = 0.1$. In the last part of this paper, we study the impact of our improved differentially private mechanisms on model inversion attacks, a privacy attack introduced by Fredrikson et al. We observe that the improved tradeoff makes the resulting differentially private model more susceptible to inversion attacks. We analyze this phenomenon formally.

Connected through hyperedge HE-1 (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"
    - "adam a method for stochastic optimization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-2 (induced by source paper "differentially private empirical risk minimization revisited faster and more general"):
  with members:
    - "private incremental regression"
    - "the price of differential privacy for online learning"
    - "beyond convexity stochastic quasi convex optimization"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private online learning"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "logan membership inference attacks against generative models"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "generating differentially private datasets using gans"
    - "towards demystifying membership inference attacks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "understanding membership inferences on well generalized learning models"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "began boundary equilibrium generative adversarial networks"
    - "knock knock who s there membership inference on aggregate location data"

Connected through hyperedge HE-4 (induced by source paper "privacy risk in machine learning analyzing the connection to overfitting"):
  with members:
    - "machine learning models that remember too much"
    - "understanding deep learning requires rethinking generalization"
    - "membership inference attacks against machine learning models"
    - "data dependent initializations of convolutional neural networks"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "curie policy based secure data exchange"):
  with members:
    - "large scale distributed neural network training through online distillation"
    - "differentially private federated learning a client level perspective"
    - "achieving secure and differentially private computations in multiparty settings"
    - "a survey on homomorphic encryption schemes theory and implementation"
    - "federated multi task learning"
    - "inherit differential privacy in distributed setting multiparty randomized function computation"
    - "extending detection with forensic information"
    - "patient driven privacy control through generalized distillation"

Connected through hyperedge HE-6 (induced by source paper "naughton s wisconsin bibliography a brief guide"):
  with members:
    - "on load shedding in complex event processing"
    - "the case for a structured approach to managing unstructured data"
    - "the lowell database research self assessment"
    - "the asilomar report on database research"

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)