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 "towards information privacy for the internet of things"):
  Abstract: In an Internet of Things network, multiple sensors send information to a fusion center for it to infer a public hypothesis of interest. However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. Each sensor makes a private observation and utilizes a local sensor decision rule or privacy mapping to summarize that observation before sending it to the fusion center. Without assuming knowledge of the joint distribution of the sensor observations and hypotheses, we adopt a nonparametric learning approach to design local privacy mappings. We introduce the concept of an empirical normalized risk, which provides a theoretical guarantee for the network to achieve information privacy for the private hypothesis with high probability when the number of training samples is large. We develop iterative optimization algorithms to determine an appropriate privacy threshold and the best sensor privacy mappings, and show that they converge. Numerical results on both synthetic and real data sets suggest that our proposed approach yields low error rates for inferring the public hypothesis, but high error rates for detecting the private hypothesis.

Includes members:
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "managing your private and public data bringing down inference attacks against your privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  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 "privacy in the internet of things threats and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy in internet of things from principles to technologies")
  Member "distributed localization of a rf target in nlos environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "geo indistinguishability differential privacy for location based systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  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)