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 publication of location entropy"
  Abstract: Location entropy (LE) is a popular metric for measuring the popularity of various locations (e.g., points-of-interest). Unlike other metrics computed from only the number of (unique) visits to a location, namely frequency, LE also captures the diversity of the users' visits, and is thus more accurate than other metrics. Current solutions for computing LE require full access to the past visits of users to locations, which poses privacy threats. This paper discusses, for the first time, the problem of perturbing location entropy for a set of locations according to differential privacy. The problem is challenging because removing a single user from the dataset will impact multiple records of the database; i.e., all the visits made by that user to various locations. Towards this end, we first derive non-trivial, tight bounds for both local and global sensitivity of LE, and show that to satisfy e-differential privacy, a large amount of noise must be introduced, rendering the published results useless. Hence, we propose a thresholding technique to limit the number of users' visits, which significantly reduces the perturbation error but introduces an approximation error. To achieve better utility, we extend the technique by adopting two weaker notions of privacy: smooth sensitivity (slightly weaker) and crowd-blending (strictly weaker). Extensive experiments on synthetic and real-world datasets show that our proposed techniques preserve original data distribution without compromising location privacy.

Connected through hyperedge HE-1 (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"
    - "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"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-2 (induced by source paper "differential privacy for evolving almost periodic datasets with continual linear queries application to energy data privacy"):
  with members:
    - "private continual release of real valued data streams"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "what does the crowd say about you evaluating aggregation based location privacy"

Connected through hyperedge HE-3 (induced by source paper "differentially private data generative models"):
  with members:
    - "performing co membership attacks against deep generative models"
    - "differentially private mixture of generative neural networks"
    - "knock knock who s there membership inference on aggregate location data"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "learning privately from multiparty data"
    - "membership inference attacks against machine learning models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "towards vision based deep reinforcement learning for robotic motion control"

Connected through hyperedge HE-4 (induced by source paper "what does the crowd say about you evaluating aggregation based location privacy"):
  with members:
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "quantifying differential privacy under temporal correlations"
    - "privacy friendly mobility analytics using aggregate location data"
    - "efficient private statistics with succinct sketches"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "haze privacy preserving real time traffic statistics"
    - "geo indistinguishability differential privacy for location based systems"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

Connected through hyperedge HE-5 (induced by source paper "knock knock who s there membership inference on aggregate location data"):
  with members:
    - "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"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "membership inference attacks against machine learning models"

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)