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: "collecting and analyzing multidimensional data with local differential privacy"
  Abstract: Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.

Connected through hyperedge HE-1 (induced by source paper "locally private distributed reinforcement learning"):
  with members:
    - "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"
    - "local differential privacy for deep learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "comparing population means under local differential privacy with significance and power"
    - "robust adversarial reinforcement learning"
    - "noisy networks for exploration"
    - "collecting telemetry data privately"
    - "epopt learning robust neural network policies using model ensembles"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving boosting in the local setting"):
  with members:
    - "locally differentially private naive bayes classification"
    - "differentially private draw and discard machine learning"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "clustering algorithms for the centralized and local models"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "crowd ml a privacy preserving learning framework for a crowd of smart devices"

Connected through hyperedge HE-3 (induced by source paper "input perturbation a new paradigm between central and local differential privacy"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "differentially private bayesian linear regression"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "efficient private erm for smooth objectives"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-4 (induced by source paper "bisample bidirectional sampling for handling missing data with local differential privacy"):
  with members:
    - "practical locally private heavy hitters"
    - "collecting telemetry data privately"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "locally differentially private data collection and analysis"):
  with members:
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "linear queries estimation with local differential privacy"
    - "locally private gaussian estimation"
    - "locally private mean estimation z test and tight confidence intervals"
    - "differentially private mixture of generative neural networks"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-6 (induced by source paper "federated learning with differential privacy algorithms and performance analysis"):
  with members:
    - "on safeguarding privacy and security in the framework of federated learning"
    - "adaptive task allocation for asynchronous federated mobile edge learning"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "the value of collaboration in convex machine learning with differential privacy"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "scheduling policies for federated learning in wireless networks"

Connected through hyperedge HE-7 (induced by source paper "aggregating votes with local differential privacy usefulness soundness vs indistinguishability"):
  with members:
    - "tight lower bounds for locally differentially private selection"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "tight lower bounds for differentially private selection"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "to drop or not to drop robustness consistency and differential privacy properties of dropout"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving crowd guided ai decision making in ethical dilemmas"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "deep tractable probabilistic models for moral responsibility"
    - "building ethics into artificial intelligence"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "a voting based system for ethical decision making"
    - "the social dilemma of autonomous vehicles"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "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.CR(Cryptography and Security)