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: "on sampling anonymization and differential privacy or k anonymization meets differential privacy"
  Abstract: This paper aims at answering the following two questions in privacy-preserving data analysis and publishing: What formal privacy guarantee (if any) does $k$-anonymization provide? How to benefit from the adversary's uncertainty about the data? We have found that random sampling provides a connection that helps answer these two questions, as sampling can create uncertainty. The main result of the paper is that $k$-anonymization, when done "safely", and when preceded with a random sampling step, satisfies $(\epsilon,\delta)$-differential privacy with reasonable parameters. This result illustrates that "hiding in a crowd of $k$" indeed offers some privacy guarantees. This result also suggests an alternative approach to output perturbation for satisfying differential privacy: namely, adding a random sampling step in the beginning and pruning results that are too sensitive to change of a single tuple. Regarding the second question, we provide both positive and negative results. On the positive side, we show that adding a random-sampling pre-processing step to a differentially-private algorithm can greatly amplify the level of privacy protection. Hence, when given a dataset resulted from sampling, one can utilize a much large privacy budget. On the negative side, any privacy notion that takes advantage of the adversary's uncertainty likely does not compose. We discuss what these results imply in practice.

Connected through hyperedge HE-1 (induced by source paper "data dependent differentially private parameter learning for directed graphical models"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "differentially private bayesian inference for exponential families"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "dp em differentially private expectation maximization"
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "understanding deep learning requires rethinking generalization"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-2 (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-3 (induced by source paper "federated heavy hitters discovery with differential privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "the privacy blanket of the shuffle model"
    - "towards federated learning at scale system design"
    - "federated learning strategies for improving communication efficiency"
    - "pripearl a framework for privacy preserving analytics and reporting at linkedin"
    - "practical locally private heavy hitters"
    - "differentially private federated learning a client level perspective"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-4 (induced by source paper "videodp a universal platform for video analytics with differential privacy"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "simple online and realtime tracking with a deep association metric"
    - "quantifying differential privacy under temporal correlations"
    - "mot16 a benchmark for multi object tracking"
    - "faceless person recognition privacy implications in social media"
    - "defeating image obfuscation with deep learning"
    - "pufferfish privacy mechanisms for correlated data"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "local differential privacy a tutorial"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "heavy hitters and the structure of local privacy"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "privately answering classification queries in the agnostic pac model"):
  with members:
    - "pac learning with stable and private predictions"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "learning privately from multiparty data"
    - "differentially private release and learning of threshold functions"
    - "learning privately with labeled and unlabeled examples"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "under the hood of membership inference attacks on aggregate location time series"):
  with members:
    - "on the lack of location privacy in crowdsourcing applications"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "differentially private publication of location entropy"
    - "membership inference attacks against machine learning models"
    - "privacy friendly mobility analytics using aggregate location data"
    - "geo spotting mining online location based services for optimal retail store placement"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving active learning on sensitive data for user intent classification"):
  with members:
    - "leveraging crowdsourcing data for deep active learning an application learning intents in alexa"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "learning privately from multiparty data"
    - "differential privacy and machine learning a survey and review"
    - "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)