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: "local obfuscation mechanisms for hiding probability distributions"
  Abstract: We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy as the local differential privacy for probability distributions. Roughly, the distribution privacy of a local obfuscation mechanism means that the attacker cannot significantly gain any information on the distribution of the mechanism’s input by observing its output. Then we show that existing local mechanisms can hide input distributions in terms of distribution privacy, while deteriorating the utility by adding too much noise. For example, we prove that the Laplace mechanism needs to add a large amount of noise proportionally to the infinite Wasserstein distance between the two distributions we want to make indistinguishable. To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output. Then we apply this mechanism to the protection of user attributes in location based services. By experiments, we demonstrate that the tupling mechanism outperforms popular local mechanisms in terms of attribute obfuscation and service quality.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving multiple tensor factorization for synthesizing large scale location traces"):
  with members:
    - "on the lack of location privacy in crowdsourcing applications"
    - "the long road to computational location privacy a survey"
    - "evaluating differentially private machine learning in practice"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "plausible deniability for privacy preserving data synthesis"
    - "fast differentially private matrix factorization"
    - "time distortion anonymization for the publication of mobility data with high utility"
    - "algorithms initializations and convergence for the nonnegative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "local distribution obfuscation via probability coupling"):
  with members:
    - "sok differential privacies"
    - "profile based privacy for locally private computations"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "renyi differential privacy"
    - "on the compositionality of quantitative information flow"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-3 (induced by source paper "statistical epistemic logic"):
  with members:
    - "hybrid statistical estimation of mutual information and its application to information flow"
    - "towards logical specification of statistical machine learning"
    - "hyperpctl a temporal logic for probabilistic hyperproperties"
    - "on the anonymization of differentially private location obfuscation"
    - "proving differential privacy in hoare logic"
    - "statistical model checking an overview"
    - "an epistemic approach to coercion resistance for electronic voting protocols"
    - "anonymity and information hiding in multiagent systems"

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)