human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the relationship between inference and data privacy in decentralized iot networks"):
  Abstract: In a decentralized Internet of Things (IoT) network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from abusing the sensor information, each sensor sanitizes its local observation using a local privacy mapping, which is designed to achieve both inference privacy of a private hypothesis and data privacy of the sensor raw observations. Various inference and data privacy metrics have been proposed in the literature. We introduce the concepts of privacy implication and non-guarantee to study the relationships between these privacy metrics. We propose an optimization framework in which both local differential privacy (data privacy) and information privacy (inference privacy) metrics are incorporated. In the parametric case where sensor observations' distributions are known \emph{a priori}, we propose a two-stage local privacy mapping at each sensor, and show that such an architecture is able to achieve information privacy and local differential privacy to within the predefined budgets. For the nonparametric case where sensor distributions are unknown, we adopt an empirical optimization approach. Simulation and experiment results demonstrate that our proposed approaches allow the fusion center to accurately infer the public hypothesis while protecting both inference and data privacy.

Includes members:
  Member "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "whose opinion to follow in multihypothesis social learning a large deviations perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "on the relation between identifiability differential privacy and mutual information privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "learning in hierarchical social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asynchronous majority dynamics in preferential attachment trees")
  Member "differentially private iterative synchronous consensus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy preserving distributed machine learning via local randomization and admm perturbation")
  Member "privacy against statistical inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "the value of feedback in decentralized detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "decentralized detection with robust information privacy protection")

Question: Please tell me which class the source paper 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.IT(Information Theory)